被覆に基づくラフ集合を用いた推薦システムに関す
る研究
メタデータ

言語: English
出版者:
公開日: 2018-05-24
キーワード (Ja): 推薦システム, 協調フィルタリング,
被覆に基づくラフ集合, カバー縮約, 個人化推薦,
分散重み付けキーワード (En): Recommender systems, Collaborative
filtering, Covering-based rough sets, Covering
reduction, Personalized recommendations,
Item-variance weighting
作成者: 張, 志鵬
メールアドレス:所属:

URL

https://doi.org/10.15118/00009628

M URORAN I NSTITUTE OF T ECHNOLOGYD OCTORAL T HESISA study on recommender systems basedon covering-based rough sets

Author:Zhipeng Z HANG

Supervisor:
Dr. Yasuo K UDO

Submitted to Muroran Institute of Technology
Division of Engineering
In partial fulfillment of the requirements for the degree ofDoctor of EngineeringinAdvanced Information and Electronic Engineering

September 20, 2017

iii

Declaration of Authorship

I, Zhipeng Z HANG, declare that this thesis titled, “A study on recommender systems
based on covering-based rough sets” and the work presented in it are my own. I
confirm that:• This work was done wholly or mainly while in candidature for a research degree at this University.
• Where any part of this thesis has previously been submitted for a degree or
any other qualification at this University or any other institution, this has beenclearly stated.
• Where I have consulted the published work of others, this is always clearly
attributed.
• Where I have quoted from the work of others, the source is always given. With
the exception of such quotations, this thesis is entirely my own work.
• I have acknowledged all main sources of help.
• Where the thesis is based on work done by myself jointly with others, I havemade clear exactly what was done by others and what I have contributed myself.

Signed:Date:v

Acknowledgements
First of all, I would like to express my sincere and heartfelt gratitude
to Professor Yasuo Kudo, for being my supervisor in my doctoral period.
The current study would not be accomplished without his support, untiring
guidance, long hours of discussions. Whenever I have problems in my research, Mr. Kudo always patiently tutor and teach me how to analyze andsolve problems. He also helps me revise the content over and over again in
the process of thesis writing. I have learned a lot of skills in writing from hiskind guidance. His lofty personality will alway inspire me to do positively
in my future pursuits.
Likewise, my deepest thanks to the members of Inference System Laboratory, including past members for all their supports, guidances, and assistances in my research work. Good atmosphere of laboratory helped me
promote my research smoothly. My sincere thanks to Satoshi Takahashi forteaching me Japanese language and common sense of life in my initial stageof life in Muroran. Whenever I have problems in my life, Takahashi always
comes first and helps me. Especially, he patiently corrected many mistakes
in my Japanese learning. I wish him a nice life and future.
Also, I would like to give my grateful appreciation to NANJO Auto Interior Co., Ltd for giving me the opportunity to have one month internship.
I came into contact with many new technologies and approaches, and also
understood the meaning of my area of research. My heartfelt gratitude to
Okamoto, Yoshida, Kikuchi for helping me to be familiar with the company’sbusiness processes, and taking me to understand and appreciate the history
and culture of Hiroshima city. I wish everything goes well with their workand the company will get better and better.
Finally, to my dear wife Yao Zhang and beloved parents, my profoundappreciation for the continuous support, understanding, guidance, encouragement and love. Thank you for giving me much-needed confidence to face
the future and my endeavors at my own feet.

vii

Abstract
With the rapid development of Internet, the human race has entered the information society and the network era. Internet could provide people with more and more
information and services; however, people have to face enormous data and uselessinformation when they enjoy the convenience brought by Internet. Recommendersystem (RS) has emerged in response to this challenge, which can advise users when
making decisions and help users discover items they might not find by themselves.
Collaborative filtering (CF) approach is popularly used in RSs owing to its satisfactory performance. Generally speaking, user-based collaborative filtering (UBCF)
and item-based collaborative filtering (IBCF) are two significant approaches in CF,
they have been successfully applied to many commercial RSs. However, with various kinds of data and complicated application environment, CF approaches are facing many challenges. For instance, UBCF cannot provide recommendations for an
active user with satisfactory accuracy and diversity simultaneously. Personalizedrecommendations cannot be provided by UBCF for a new user which often has insufficient information. In addition, items that make a more significant contribution
cannot have high weighting in IBCF. In view of the above key issues, this dissertation
launched a study of the following aspects:(1) Aiming to provide personalized recommendations for an active user, we apply covering-based rough sets to improve UBCF, and propose a new covering-based
collaborative filtering (CBCF) approach. CBCF inserts a user reduction procedure
into UBCF, covering reduction in covering-based rough sets is utilized to remove redundant users from all users. Then, k-nearest neighbors are selected from candidateneighbors comprised by the reduct-users. Our experiment results suggest that, forthe sparse datasets that often occur in real RSs, CBCF outperforms than the UBCF,
and can provide satisfactory accuracy and coverage for an active user at the sametime.
(2) In order to provide personalized recommendations for a new user, through adetailed analysis of the characteristic of new users, we reconstruct a decision classto improve the previous CBCF. Unlike the previous CBCF, the decision class in improved CBCF can be extracted easily from the user-item rating matrix. Furthermore,
the improved CBCF could provide personalized recommendations without needing
special additional information. Our experiment results suggest that the improved
CBCF significantly outperforms those of existing work and can provide personalized recommendations for a new user with satisfactory accuracy and diversity simultaneously.
(3) The traditional IBCF approach treats all items as the same weighting; however, because some items may have more important impact when computing thesimilarity and predictions, item-variance weighting should also be considered. In
this paper, we present the time-based correlation degree and covering degree, andapply them to the traditional IBCF approach to rearrange the item weighting. Our
experimental results suggest that, our proposed approach can produce recommendations superior to the traditional IBCF and other existing work.

Keywords: Recommender systems; Collaborative filtering; Covering-based roughsets; Covering reduction; Personalized recommendations; Item-variance weighting.

ix

Contents
Declaration of Authorshipiii

Acknowledgementsv

Abstractvii

List of Figuresxi

List of Tablesxiii

1 Introduction
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.1 User-based collaborative filtering . . . . . . . . . . . . . . . . . .
1.2.2 Item-based collaborative filtering . . . . . . . . . . . . . . . . . .
1.3 Research problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.4 Research contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.5 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1
1
2
2
4
4
5
6

2 Covering-based rough sets
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Basic definitions and concepts . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Covering reduction algorithms . . . . . . . . . . . . . . . . . . . . . . .
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7
7
7
8
9

3 CBCF for active users’ personalized recommendations
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Analysis and problem setting . . . . . . . . . . . . . . . . . . . . . . . .
3.4 CBCF for an active user’s personalized recommendations . . . . . . . .
3.4.1 Motivation of CBCF approach . . . . . . . . . . . . . . . . . . .
3.4.2 Procedures of CBCF approach . . . . . . . . . . . . . . . . . . .
3.4.3 Example of CBCF approach in RSs . . . . . . . . . . . . . . . . .
3.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Experiments and evaluations . . . . . . . . . . . . . . . . . . . . . . . .
3.5.1 Experimental setup and evaluation metrics . . . . . . . . . . . .
3.5.2 Experimental results and comparisons . . . . . . . . . . . . . . .
3.5.3 Analysis and discussion . . . . . . . . . . . . . . . . . . . . . . .
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11
11
12
13
14
14
14
15
17
18
18
19
23
23

4 Improved CBCF for new users’ personalized recommendations
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Analysis and problem setting . . . . . . . . . . . . . . . . . . . . . . . .

25
25
26
26

x

4.4

4.5

4.6

4.3.1 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.2 Problem setting . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Improved CBCF for a new user’s personalized recommendations . . .
4.4.1 Motivation of improved CBCF approach . . . . . . . . . . . . .
4.4.2 Reconstruction of decision class for a new user . . . . . . . . . .
4.4.3 Procedures of improved CBCF approach . . . . . . . . . . . . .
4.4.4 Comparisons between the previous CBCF and improved CBCF
Experiments and evaluations . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1 Experimental setup and evaluation metrics . . . . . . . . . . . .
4.5.2 Experimental results and comparisons . . . . . . . . . . . . . . .
4.5.3 Analysis and discussion . . . . . . . . . . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27
32
32
32
33
33
34
35
35
38
54
55

5 Item-variance weighting for IBCF by using time-related correlation degreeand covering degree
57
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.2 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.3 Analysis and problem setting . . . . . . . . . . . . . . . . . . . . . . . . 58
5.4 Time-related correlation degree and covering degree for the traditional
IBCF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.4.1 Motivation of proposed approach . . . . . . . . . . . . . . . . . 59
5.4.2 Time-related correlation degree and covering degree . . . . . . 59
5.4.3 Procedures of proposed approach . . . . . . . . . . . . . . . . . 60
5.5 Experiments and evaluations . . . . . . . . . . . . . . . . . . . . . . . . 60
5.5.1 Experimental setup and evaluation metrics . . . . . . . . . . . . 60
5.5.2 Experimental results and comparisons . . . . . . . . . . . . . . . 62
5.5.3 Analysis and discussion . . . . . . . . . . . . . . . . . . . . . . . 62
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6 Conclusions and future work67
6.1 Thesis summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.2 Future research directions . . . . . . . . . . . . . . . . . . . . . . . . . . 68
Bibliography

69

A Papers published during the doctoral period

73

xi

List of Figures
3.1
3.2
3.3
3.4
4.1
4.2
4.3

Accuracy results (MAE and RMSE) versus the size of neighborhoodwith MovieLens dataset . . . . . . . . . . . . . . . . . . . . . . . . . . .
Coverage results versus the size of neighborhood with MovieLens
dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Accuracy results (MAE and RMSE) versus the size of neighborhoodwith Jester dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Coverage results versus the size of neighborhood with Jester dataset .

Proportion of rating scores on popular items in the MovieLens dataset
Proportion of rating scores on popular items in the Netflix dataset . . .
Percentage of ratings on popular items by users with no more than nratings in the MovieLens dataset . . . . . . . . . . . . . . . . . . . . . .
4.4 Percentage of ratings on popular items by users with no more than nratings in the Netflix dataset . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Result of MAE measure on the MovieLens dataset . . . . . . . . . . . .
4.6 Result of MAE measure on the Netflix dataset . . . . . . . . . . . . . .
4.7 Result of RMSE measure on the MovieLens dataset . . . . . . . . . . .
4.8 Result of RMSE measure on the Netflix dataset . . . . . . . . . . . . . .
4.9 Result of MAE measure on the Jester dataset . . . . . . . . . . . . . . .
4.10 Result of RMSE measure on the Jester dataset . . . . . . . . . . . . . . .
4.11 Result of coverage measure on the MovieLens dataset . . . . . . . . . .
4.12 Result of coverage measure on the Netflix dataset . . . . . . . . . . . .
4.13 Result of coverage measure on the Jester dataset . . . . . . . . . . . . .
4.14 Result of MP measure on the MovieLens dataset . . . . . . . . . . . . .
4.15 Result of MP measure on the Netflix dataset . . . . . . . . . . . . . . . .
4.16 Result of MP measure on the Jester dataset . . . . . . . . . . . . . . . .
4.17 Result of MN measure on the MovieLens dataset . . . . . . . . . . . . .
4.18 Result of MN measure on the Netflix dataset . . . . . . . . . . . . . . .
4.19 Result of MN measure on the Jester dataset . . . . . . . . . . . . . . . .
5.1
5.2
5.3

20
21
22
22
28
29
30
31
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53

Precision of the TCIBCF, TWIBCF, and IBCF against the number ofrecommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Recall for the TCIBCF, TWIBCF, and IBCF against the number of recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
F1 for the TCIBCF, TWIBCF, and IBCF versus the number of recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

xiii

List of Tables
3.1
3.2
3.3
3.4
3.5
4.1
4.2
4.3
4.4
4.5
5.1
5.2

Example of user-item rating matrix RM . . . . . . . . . . . . . . . . . .
Example of item attribute matrix AM . . . . . . . . . . . . . . . . . . .
Example of similarity and rank depending on different approaches . .
Average size of decision class versus l with the MovieLens dataset . .
Number of candidate neighbors for traditional UBCF and CBCF approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16
16
16
18

Proportion of items and ratings in the MovieLens dataset . . . . . . . .
Proportion of items and ratings in the Netflix dataset . . . . . . . . . .
Experimental items versus original data in the MovieLens dataset . . .
Experimental items versus original data in the Netflix dataset . . . . .
Number of candidate neighbors for the traditional UBCF and CBCF
approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27
27
37
37

20

38

Example of user-item rating matrix RM . . . . . . . . . . . . . . . . . . 58
Results of MAE and RMSE metrics . . . . . . . . . . . . . . . . . . . . . 66

1

Chapter 1

Introduction
1.1 Background
Rapid internet, economic, and technological developments have led to the dramatical growth of the data and information. Internet provides people with more andmore information and services and it broke the limit of space and time of traditional
life and learning. People can shop on the internet conveniently and study via internet whenever and wherever. However, people have to face enormous data anduseless information when they enjoy the convenience brought by internet. This is
the famous "information overload" problem (Hiltz and Turoff, 1985; Malone et al.,
1987; Edmunds and Morris, 2000). Therefore, more customers are facing the problem of discovering the demanded contents from overwhelmingly massive data. As
the result, this problem becomes a popular research topic and attracts attention fromlots of scientists.
Generally, there are many stages for users to maintain information from internet.
For instance, various portal sites are established, such as yahoo and so on. They help
users filter and organize a variety of popular resource and information to discoverand browse. However, the organized information is not always able to meet users’
need, as well as overwhelming data will make the website overstaffed with the explosive growth of data, which results in the incompletion of information retrieval(Chang and Wang, 2011). In addition, search engines start to emerge so that users
are able to retrieve their desired contents, such as google. But the accuracy of search
results quite depends on the description towards questions, which is usually notquite precise, thus the caused bias will make it difficult for users to identify exactly
their required results (Höchstötter and Lewandowski, 2009).
Recommender system (RS) analyzes the personal behavior of customers to learn
their preferences and then recommends products (e.g., books, CDs, movies, andnews) that may interest them (Adomavicius and Tuzhilin, 2005; Bobadilla et al.,2013). Currently, RSs are widely used applications in daily life. When users arelack of experience in the related field or can not deal with the huge amount of information, RSs could provide an intelligent information filtering for these users. For
example, Amazon uses RSs to provide users with on-line shopping, because users’interests are usually different, so recommendation results need to be personalized,that is to say different users will receive different recommendations. In most of RSs,
recommendation results are often presented in a sorted list, the sort number of item
is determined by the target user’s interest. In order to predict the target user’s preference, RSs match the user’s personal information (e.g., age, gender, education) anditem’s characteristics, or collect the user’s historical information to make predictions.
Generally, active users and new users are two types of users in RSs. An active user

2

Chapter 1. Introduction

has rated a lot of items, so RSs can utilize these sufficient information to provide reliable recommendations; however, a new user often has very few ratings, it will increase the recommendation difficulty. In recent years, their great commercial value
and research potential have rendered RSs increasingly significant in recent years(Kotkov, Wang, and Veijalainen, 2016; Lu et al., 2015).
Currently, collaborative filtering (CF) is one of the most important approaches
in RSs, that uses a customer’s history as the basis of decision making (Symeonidis
et al., 2008; Hameed, Al Jadaan, and Ramachandram, 2012). CF approach was firstpresented by Goldberg (Goldberg et al., 1992), which assumes that users who have
similar preferences in the past will tend to have similar tastes in the future, or anitem will be preferred by a user if this item is similar with the preference of thisuser in the past (Herlocker, Konstan, and Riedl, 2002), and many well-known online service providers are adopting this approach, including Amazon, YouTube, andGoogle News. CF approach does not need to analyze items’ content information,
which allows users to rate items according to their own experience and preference,then utilizes users’ rating records to provide personalized recommendations for thetarget user (Herlocker et al., 1999). CF uses not only the target user’s information,
but also information of other users to filter out clutter and irrelevant information.
Therefore, CF could provide recommendations with high quality, and help users findtheir potential interests. Nowadays, CF is proved to be one of the most successful
personalized recommendation approaches, and has been widely utilized in the fieldof modern RSs.
To introduce CF approach, we first present some CF-related notations and terminologies used in this thesis. Given an RS, let U and I be finite sets of users anditems, respectively, R ∪ {⋆} the set of possible item rating scores, and RM the useritem rating matrix, AM the item attribute matrix. Absence of a rating is indicated byan asterisk (⋆). The rating score of user u for item i is denoted by ru,i ∈ R ∪ {⋆}, andthe average of the valid ratings of user u is denoted by ru¯ u , the average ratings of¯ u . θ is set as the threshold for rating scores, and items
the i-th item is denoted by riwith ru,i ≥ θ are defined as items that are relevant to user u. Iu = {i ∈ I|ru,i ̸= ⋆} isthe set of all items rated by user u, and Iuc is the complementary set of Iu , indicating
items that have not yet been rated by user u.

1.2 Research objectives
CF has many forms, user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF) are two significant forms in CF. In this section, we present
the detailed information about UBCF and IBCF.

1.2.1 User-based collaborative filtering
UBCF approach was first proposed by Herlocker (Herlocker et al., 1999), which assumes that users who have similar preferences in the past will tend to have similar
tastes in the future. UBCF can provide satisfactory recommendations utilizing only
the user’s historical ratings, without requiring any other special information, and it
has demonstrated remarkable success in RSs. The traditional UBCF can be separated
into four steps:
Step 1: Similarity computation. A target user tu’s set of candidate neighbors
CN tu includes all users. Based on historical rating information, compute the similarity between each user u ∈ CN tu and the target user tu. Here the Pearson correlation

1.2. Research objectives

3

coefficient approach (1.1) is popularly used as a similarity measure:∑¯ tu ) (ru,i − ru
¯ u)i∈Itu ∩Iu (rtu,i − ru√∑sim(tu, u) = √∑,22
¯ tu )¯ u)i∈Itu ∩Iu (rtu,i − rui∈Itu ∩Iu (ru,i − ru

(1.1)

where sim(tu, u) indicates the similarity between the target user tu and a user u ∈CN tu . Itu = {i ∈ I|rtu,i ̸= ⋆} is the set of all items rated by target user tu, and ru¯ tu isthe average rating of target user tu:
∑
rtu,i
ru
¯ tu = i∈Itu,(1.2)|Itu |
where |Itu | is the cardinality of the set Itu .
Step 2: Neighborhood selection. Select the k most similar (nearest) users from
CNtu to comprise the neighborhood Ntu (k) for the target user tu.
Step 3: Rating prediction. Normalize ratings and according to the rating information of neighborhoods, predict a rating score ptu,i for each item i in unrated item
c of the target user tu. The weighted sum approach (1.3) is often used in RSs to
set Itupredict ratings:∑
ptu,i = λsim(tu, u) ∗ ru,i ,(1.3)
u∈Ntu (k)∩Ui

where ptu,i is the prediction of item i for target user tu, Ui = {u ∈ U |ru,i ̸= ⋆} is theset of users who have rated item i, and multiplier λ is a normalizing factor selected
as
1
λ= ∑
.
(1.4)
u∈Ntu (k)∩Ui sim(tu, u)
Step 4: Item recommendation. According to the predicted rating scores, selectthe top N items that have the highest ptu,i from the candidate items as the recommendations to be provided for target user tu.
Note that, within specific systems, these steps may overlap or the order may beslightly different. Algorithm 1.1 summarizes the traditional UBCF approach.
Algorithm 1.1 Traditional UBCF approach
Input: User-item rating matrix RM and a target user tu.
Output: Recommended items set of size N for the target user tu.
k : Number of users in the neighborhood Ntu (k) of the target user tu.
N : Number of items recommended to the target user tu.
c : Items that have not yet been rated by the target user tu.
Itu
CN tu : Candidate neighbors of the target user tu.
ptu,i : Rating prediction of item i for the target user tu.
1: CN tu = U , then compute the similarity between target user tu and each useru ∈ CN tu ;c do
2: for each item i ∈ Itu
3:
Find the k most similar users in CN tu to comprise neighborhood Ntu (k);4:
Predict rating score ptu,i for item i by neighborhood Ntu (k);5: end for
6: Recommend to the target user tu the top N items having the highest ptu,i .

Chapter 1. Introduction

4

1.2.2

Item-based collaborative filtering

IBCF approach was first proposed by Sarwar (Sarwar et al., 2001) and is now a significant approach widely used in RSs, which assumes that items which are similar
with a user’s preferred item will also be preferred by this user. IBCF has good scalability and can be applied to the huge numbers of items and users that are typical ofmodern RSs. IBCF can easily handle large data sets and produce better predictions
than UBCF. Also in contrast to UBCF, IBCF is able to compute item-item similarity
off-line, both saving on-line time and making more effective recommendations. The
detailed procedures of IBCF are as follows:
Step 1: Item-item similarity computation. Based on the information of user-item
rating matrix RM , IBCF computes the similarity between every item. Several algorithms can be used to make the similarity computation, but the Pearson correlation
coefficient is one of the most widely used measure in IBCF:
sim(x, y) ∑= √∑u∈Ux ∩Uy (ru,x −rix )∗(ru,y −riy )
√∑
¯ 2
¯ 2
u∈Ux ∩Uy (ru,x −rix )u∈Ux ∩Uy (ru,y −riy )

¯

¯

.

(1.5)

¯x
Here, Ux = {u ∈ U |ru,x ̸= ⋆} is the set of all users who have rated item x and ri
is the average rating of the x-th item.
Step 2: Neighborhood selection. After computing the item-item similarity, selectthe k most similar (nearest) items from similarity list to comprise the neighborhood
Ni (k) for the item i ∈ I.
Step 3: Rating prediction. Normalize ratings and according to the rating information of Ni (k) from the target user tu, predict a rating score ptu,i for item i ∈ I. The
weighted sum is a very useful measure used in the IBCF:∑
j∈S ∩I sim(i, j) ∗ rtu,jptu,i = ∑ i tu,
(1.6)
j∈Si ∩Itu |sim(i, j)|here Itu = {x ∈ I|rtu,x ̸= ⋆} is the set of items that target user tu has rated, Si is theset of items that are similar to item i, and ptu,i is the prediction made about item i fortarget user tu.
Step 4: Item recommendations. According to the predicted rating scores, selecttop N items which have the highest predicted rating score ptu,i from the candidate
items as the recommendations to be provided for target user tu.
Note that, within specific systems, these steps may overlap or the order may
be slightly different. Algorithm 1.2 presents concise steps of the traditional IBCF
approach.

1.3 Research problems
In this thesis, we make research about the following three main problems about CF:
1. For an active user who often has sufficient rating information, the traditional
UBCF cannot provide personalized recommendations with good accuracy anddiversity simultaneously;
2. For a new user who often has fewer ratings, the traditional UBCF cannot provide personalized recommendations with good diversity while maintaining
adequate accuracy;

1.4. Research contributions

5

Algorithm 1.2 Traditional IBCF approach
Input: User-Item rating matrix RM and an target user tu.
Output: Recommended items set of size N for the target user tu.
Ni (k) : Neighborhood of the item i.
k : Number of items in the neighborhood Ni (k) of the item i.
N : Number of items recommended to the target user tu.
c : Items which have not yet rated by the target user tu.
Itu
ptu,i : Rating prediction of item i for the target user tu.
1: Compute similarity between each item in I;
c do
2: for each item i ∈ Itu
3:
Find the k most similar items of item i to comprise neighborhood Ni (k);4:
Predict rating score ptu,i for item i by the ratings of neighborhood Ni (k) fromthe target user tu;
5: end for
6: Recommend to the target user tu the top N items having the highest predicted
rating scores ptu,i .

3. For items which has different contributions in computing similarity and predictions, the traditional IBCF approach cannot make item-variance weightingwith respect to their contributions.
For the first and second problems, that is because some studies have shown thatthe traditional UBCF approach cannot provide satisfactory recommendations withgood accuracy and diversity values simultaneously. In addition, resent research has
concluded that gains diversity in RS can frequently be accompanied by losses in accuracy, making it difficult to select a reasonable trade-off between accuracy and diversity. Moreover, a new user in RSs usually has fewer ratings, information obtained
from a new user is insufficient, it increases the difficulty of recommendations.
For the third problem, that is because in traditional IBCF approach, all items
carry the same weight when computing the similarity and predictions. However, it
is widely recognized that some items are more important than others and should begiven relatively higher weighting.

1.4 Research contributions
In this thesis, there are the following three contributions with respect to three problems listed in Section 1.3:
1. In order to provide personalized recommendations for an active user, we apply covering-based rough sets to the traditional UBCF, and propose a newcovering-based collaborative filtering (CBCF) approach. CBCF inserts a user
reduction procedure into the traditional UBCF. Covering reduction in coveringbased rough sets is used to remove redundant users from all users. Then,k-nearest neighbors are selected from candidate neighbors comprised by the
reduct-users. CBCF can select more efficient neighborhood for an active user,
and provide recommendations with satisfactory accuracy and diversity simultaneously.
2. In order to provide personalized recommendations for a new user, through adetailed analysis of the characteristic of new users, we reconstruct a decisionChapter 1. Introduction

6class to improve the previous CBCF. In this way, improved CBCF can remove
redundant candidate neighbors for a new user as many as possible. Furthermore, unlike the previous CBCF, improved CBCF could provide personalizedrecommendations without needing special additional information.
3. In order to make item-variance weighting for the traditional IBCF, we present
time-related correlation degree and covering degree, and apply them to the
traditional IBCF to rearrange the item weight. The proposed novel approach
can produces recommendations results superior to those of existing work.

1.5 Outline of the thesis
This thesis is organized as follows:
Chapter 1 presents the introduction, including the research background, research
objectives, research problems and research contributions.
Chapter 2 presents the basic knowledge of covering-based rough sets which weused as our research method in this thesis, that covers the definitions of covering andcovering approximations space, and six types of covering-based rough sets with respect to the different upper and lower approximation operations. In addition, asa significant concept of covering-based rough sets, covering reduction is also discussed in this chapter.
Chapter 3 describes a CBCF approach we have proposed to provide personalizedrecommendations for an active user. First, we analyze the deficiency of traditional
UBCF and give the problem setting. Then, we apply covering-based rough sets totraditional UBCF, and propose a new CBCF approach. The motivation, detailed procedures and an example of CBCF approach are also presented. Finally, experiments
using popular datasets are made to prove that CBCF outperforms than the traditional UBCF, and can provide satisfactory accuracy and diversity simultaneously.
Chapter 4 introduces an improved CBCF approach which could provide personalized recommendations for a new user. First, we summarize the related work about
personalized recommendations for a new user. Then, through two popular datasets,
we analyze the characteristic of a new user in RSs and present the problem setting.
Next, we introduce our improve CBCF, including motivation, reconstruction of decision class and detailed procedures. Moreover, comparisons between the previous
CBCF and improved CBCF are also made. Finally, experiments are made to demonstrate that the improved CBCF significantly outperforms those of existing work, andcan provide personalized recommendations for a new user with satisfactory accuracy and diversity simultaneously.
Chapter 5 explains a TCIBCF approach we proposed to improve the traditional
IBCF by using time factor and covering degree. First, we analyze basic proceduresof traditional IBCF and present the problem setting. Then, we present time-related
correlation degree and covering degree, and apply them to the traditional IBCF to
propose a TCIBCF approach, the motivation and detailed procedures are also discussed. In the end, we design the experiments, and utilize some popular evaluation
metrics to indicate that TCIBCF approach produces recommendation results superior to those of existing work.
Finally, the conclusions and directions for future research are presented in Chapter 6.

7

Chapter 2

Covering-based rough sets
2.1 Introduction
Rough set theory was first presented by Pawlak in the early 1980s (Pawlak, 1982),
it is a very useful tool of data analysis for processing vague and uncertain data
(Pawlak and Skowron, 2007). Rough set has attracted many researcher’s interests
all over the world. The core concepts of the rough sets are lower and upper approximations based on equivalence relations, and the knowledge which hidden in
the information system can be expressed in the form of decision rules by means ofthese two concepts. However, the classical rough set based on equivalence relations
is not suitable to be applied to complex information system. Hence people proposed
different extensions of rough set theory. Covering-based rough sets extend the partition in rough sets to the covering (Yao and Yao, 2012), and use the covering of thedomain to construct the lower and upper approximations. In this way, rough setsare enriched from both theory and application in terms of more complicated data.
This chapter presents an overview of the basic knowledge of covering-basedrough sets, which covers the definitions of covering and covering approximations
space. In addition, we focus on covering reduction which is a significant concept in
CBCF, and analyze main types of reduction algorithms.

2.2 Basic definitions and concepts
Pawlak’s rough sets are based on equivalence relations. Covering-based rough sets
extend a partition to a covering. In this section, we introduce some basic concepts
about covering-based rough sets. More information of covering-based rough sets
can be found in (Tsang, Degang, and Yeung, 2008; Yao and Yao, 2012; Zhu, 2009).
First we list some definitions about coverings used in this thesis.
Definition 2.1. Let T be the domain of discourse, and C a family of subsets of T . If
none of the subsets in C is empty and ∪C = T, then C is called a covering of T .
Definition 2.2. Let T be a non-empty set, and C a covering of T . We refer to theordered pair ⟨T, C⟩ as the covering approximation space.
Definition 2.3. Let ⟨T, C⟩ be a covering approximation space, x ∈ T .
M d(x) = {K ∈ C|x ∈ K ∧ (∀S ∈ C ∧ x ∈ S ∧ S ⊆ K ⇒ K = S)}is called the minimal description of x.
Definition 2.4. Let C be a covering of T , N (x) = ∩{K ∈ C|x ∈ K} is called theneighborhood of x.

8Chapter 2. Covering-based rough sets

Different lower and upper approximation operations would generate different
types of covering-based rough sets. The covering-based rough sets was first presented by Zakowski (Zakowski, 1983), who extended Pawlak’s rough set theory
from a partition to a covering. Pomykala gave the notion of the second type ofcovering-based rough sets (Pomykala, 1987), while Tsang presented the third type
(Tsang et al., 2004), Zhu defined the fourth and fifth types of covering-based roughsets (Zhu and Wang, 2012; Zhu, 2007), and Wang studied the sixth type of coveringbased approximations (Wang, Dai, and Zhou, 2004).
Definition 2.5. Let C be a covering of T , P (T ) is the power set of T . The operations
CL : P (T ) → P (T ) and CL∗ : P (T ) → P (T ) are defined as follows: ∃X ∈ P (T )CL(X) = ∪{K ∈ C|K ⊆ X} = ∪{K|∃x, s.t.(K ∈ M d(x)) ∧ (K ⊆ X)}
CL∗ (X) = {x|N (x) ∧ X} = ∪{N (x)|N (x) ∧ X}
Here CL(X) is the first, the second, the third, the fourth and the fifth covering
lower approximation operations and CL∗ (X) is the sixth covering lower approximation operations with respect to the covering C. The operations F H, SH, T H, RH, IH, XH :P (T ) → P (T ) are defined as follows: ∃X ∈ P (T )F H(X) = CL(X) ∪ (∪{M d(x)|x ∈ X − CL(X)})
SH(X) = ∪{K|K ∈ C, K ∩ X ̸= ∅}T H(X) = ∪{M d(x)|x ∈ X}
RH(X) = CL(X) ∪ (∪{K|K ∩ (X − CL(X)) ̸= ∅})IH(X) = CL(X) ∪ (∪{N (x)|x ∈ X − CL(X)}) = ∪{N (x)|x ∈ X}
XH(X) = {x|N (x) ∩ X ̸= ∅}F H, SH, T H, RH, IH, XH are called the first, the second, the third, the fourth,the fifth and the sixth covering upper approximation operations with respect to C,
respectively.

2.3 Covering reduction algorithms
Covering reduction is a significant concept in covering-based rough set theory (Yangand Li, 2010). The concept of covering reduction was originally presented by Zhu
et al. (Zhu and Wang, 2003). In this thesis, we refer to the algorithm proposed by
Zhu et al. (Zhu and Wang, 2007) as the first type of reduction algorithm, whichcorresponds to the definition of reduct(C) in (Zhu and Wang, 2007). Definition 2.6
defines this algorithm.
Definition 2.6. Let C be a covering of domain T , and K ∈ C. If K is a union of somesets in C − {K}, K is reducible in C; otherwise, K is irreducible. When all reducible
elements are removed from C, the new irreducible covering is called the first-type
reduct of C.
Zhu et al. presented two other covering reduction algorithms (Zhu and Wang,2007; Zhu and Wang, 2012), which we refer to as the second and third types of reduction algorithms, respectively. Definition 2.7 defines the second-type algorithm,
which corresponds to the definition of exclusion(C) provided by Zhu et al. (Zhu andWang, 2007). Definition 2.8 defines the third-type algorithm, which corresponds tothe definition of exact − reduct(C) (Zhu and Wang, 2012).
Definition 2.7. Let C be a covering of domain T , and K ∈ C. If there exists anotherelement K ′ of C such that K ⊂ K ′ , K is an immured element of covering C. When
we remove all immured elements from C, the set of all remaining elements is still acovering of T , and this new covering has no immured element. We refer to this newcovering as the second-type reduct of C.

2.4. Summary

9

Definition 2.8. Let C be a covering of domain T , and K ∈ C. If there exists K1 , K2 ...Km ∈C − K such that K = K1 ∪, ..., ∪Km , and ∀x ∈ K and {x} is not a singleton elementof C, K ⊆ ∪{K ′ | x ∈ K ′ ∈ C − {K}}, K is called an exact-reducible elementof C. When all exact-reducible elements are removed from C, the new irreducible
covering is called the third-type reduct of C.
Comparing the three types of covering reduction algorithms, we find that, thefirst type removes redundant elements more efficiently than the third type because
the third type has an additional restriction condition. For example, we assume that
K ∈ C is a reducible element in the first type, but if there exists x ∈ K that {x}is a singleton element of C, K is not an exact-reducible element in the third type.
However, if K ∈ C is an exact-reducible element in the third type, it must be a
reducible element in the first type.
Here, we consider the first and second types. If we assume that K ∈ C is a reducible element in the first-type algorithm, then there must be other elements whose
union is K. For example, for K = K1 ∪ K2 , only K should be removed; however,under the same conditions, in the second-type algorithm, K1 and K2 would both beconsidered as immured elements, which should be removed.
Typically, an RS has a vast number of items and each user has different preferences. Therefore it is difficult to represent one user’s preferred item set as a union ofother users’ preferred item sets accurately. In this situation, for the first-type algorithm, few reducible elements can be removed; however, for the second type, there
can be a large number of reducible elements, because RSs have a large number ofusers, it is easy to find one user’s preferred item set that includes another user’sset. Thus, the second type of covering reduction algorithm can be used to remove
more reducible elements in RSs. The second-type of covering reduction algorithm(STCRA) is given in Algorithm 2.1.
Algorithm 2.1 STCRA: The second-type of covering reduction algorithm
Input: A covering of a domain: C.
Output: An irreducible covering of a domain: reduct(C).
Ki ,Kj : Elements in the covering C.
1: set reduct(C)=C;
2: for i = 1 to card(C) do
3:for j = 1 to card(C) do
4:
if Kj ⊂ Ki then5:
if Kj ∈ reduct(C) then6:
reduct(C) = reduct(C) − {Kj };7:
end if8:
end if
9:end for
10: end for
11: return reduct(C);

2.4 Summary
In this chapter, we present the basic definitions and notions of covering-based roughsets. Including covering and covering approximation, and different types of coveringbased rough sets according to different upper and lower approximation operations.

10

Chapter 2. Covering-based rough sets

Besides that, covering reduction which could remove redundant elements is also
demonstrated. We discuss different types of reduction algorithm, and analyze theirefficiency of removing redundant users, and get the conclusion that, the second-typeof covering reduction algorithm we defined in this chapter can be used to remove
more reducible elements.

11

Chapter 3

CBCF for active users’ personalizedrecommendations
3.1 Introduction
Currently, although most studies focus on developing new approaches to improve
RS accuracy, it has been argued that using only an accuracy metric to evaluate RSs is
not sufficient and that the diversity of recommendations must also be considered as
an important evaluation measure (Kunaver and Požrl, 2017; Clarke et al., 2008; Hu
and Pu, 2011). Because in a real business environment, a company can use RSs toobtain more benefits by providing recommendations with higher diversity (Vargas,
2011; Boim, Milo, and Novgorodov, 2011; Di Noia et al., 2014). For example, as there
are many movies in the statistical long tail that have only a few ratings, it would
be profitable for Netflix if RSs would encourage users to rent movies in the longtail, because these are less costly to license and acquire from distributors than newreleases or highly popular movies. However, recent studies have shown that it isvery difficult to obtain a reasonable trade-off between the accuracy and diversity of
an RS (Liu, Shi, and Guo, 2012; Zhou et al., 2010), because increasing the diversityof recommendations is usually accompanied by a loss in accuracy (Javari and Jalili,2015).
CF is a significant component of the recommendation process (Hameed, Al Jadaan,
and Ramachandram, 2012). UBCF is one of the most useful approaches in CF. Without requiring any other special information, UBCF can only utilize user’s historicalratings to provide satisfactory recommendations. However, the traditional UBCF
is difficult to achieve good values for accuracy and diversity simultaneously (Herlocker et al., 1999; Herlocker, Konstan, and Riedl, 2002; Zhu et al., 2014). Aiming
at improving the traditional UBCF approach to obtain good values of accuracy andcoverage at the same time, in this chapter, covering-based rough set theory is applied to RSs. We propose CBCF, a new approach that uses covering reduction toremove redundant users, then neighborhood is selected from candidate neighbors
comprised by the reduct-users. Experimental results reveal that our proposed CBCF
approach provides better recommendation results than the traditional UBCF approach.
The remainder of this chapter is organized as follows. In Section 3.2, some related
works are provided. We review basic concepts involved in the traditional UBCF
approach and other work. In Section 3.3, we analyze neighborhood selection problems. In Section 3.4, we give the detailed motivation and procedures of the CBCF
approach. In Section 3.5, we describe our experiments and compare CBCF resultswith the results obtained using the traditional UBCF approach. The summary ofthis chapter is presented in Section 3.6.

12Chapter 3. CBCF for active users’ personalized recommendations

3.2 Related works
UBCF approach was first proposed by Herlocker (Herlocker et al., 1999), which relies on target user neighborhood information to make predictions and recommendations. Neighborhood selection is one crucial procedure of UBCF approach, which
selects a set of users from candidate neighbors to comprise neighborhood for an
active user. Whether appropriate neighborhood can be selected will have a direct
bearing on the rating prediction and item recommendation. In general UBCF approach, k-nearest neighbors (k-NN) approach is proved to be the best method togenerate a neighborhood, which picks the k most similar (nearest) users from candidate neighbors to comprise the neighborhood for an active user (Herlocker, Konstan,and Riedl, 2002). So we consider the k-NN UBCF approach as the traditional UBCF
approach in the rest of this chapter. More detailed information and procedures of
UBCF could be found in Subsection 1.2.1.
Currently, commercial RSs have a large number of users, neighborhood must
be composed of a subset of users rather than all users if RSs want to guarantee acceptable response time (Herlocker, Konstan, and Riedl, 2002). Accuracy measures
how closed RSs predictions reflect actual user preferences, and coverage interprets
the extent to which recommendations cover the set of available items. Both metrics
are important in RSs. In the neighborhood of traditional UBCF approach, neighbors tend to have similar tastes, so high predicted scores from them concentrate in
few types of items, even just popular items. Due to the popular items often havehigh ratings from users, so recommendations from the traditional UBCF approach
often have high accuracy. However, types of recommendations are very limited, it
leads to an unsatisfactory coverage value (Gan and Jiang, 2013). Therefore using the
traditional UBCF is difficult to achieve good values for both metrics simultaneously.
Niemann and Wolpers proposed a usage context-based collaborative filtering approach to achieve a reasonable balance between the accuracy and diversity (Niemann and Wolpers, 2013). In the procedure of similarity computation, they described an item by the items it is significantly often used with rather than by its
users or content attributes, items were similar if they often occurred in similar usage
contexts. This approach could compensate the weaknesses of traditional UBCF approach, and increased the diversity with only one case of accuracy loss. However,this approach needed additional information (e.g. user profiles) which was often notavailable or incomplete.
Gan and Jiang proposed a network-based collaborative filtering approach to improve the diversity without lowing the accuracy of recommendations (Gan and Jiang,2013). Before making the rating prediction, they constructed a user similarity network from user’s historical data by using a nearest neighbor approach. Due to thisnetwork could filter out weak relationships between users, this approach not only
enhanced the recommendation accuracy but also improved the diversity; however,the performance of this approach depending on the selected parameter, and the optimal value of parameters was still unknown. Besides, it was difficult to select asuitable mathematical model for the user similarity network.
Adomavicius and Kwon developed a sophisticated graph-theoretic approach tomaximize the diversity of recommendations based on maximum flow or maximum
bipartite matching computations (Adomavicius and Kwon, 2011). After selecting
the neighborhood, this approach predicted rating scores for un-rated items with traditional filtering approach to generate candidate items. Then, let users and items
be represented as nodes, and translated the top-N candidate items setting into agraph-theoretic framework to re-rank candidate items. Good values of accuracy and3.3. Analysis and problem setting

13

diversity from this approach depends on the selection criteria for items included in
the graph. However, the more items are selected as candidate items for each user,
the more diverse and less accurate are the recommendations and vice versa.
Adomavicius and Kwon improved the recommendation diversity by re-ranking
the candidate items using a new re-rank technology (Adomavicius and Kwon, 2012).
This approach could only utilize traditional UBCF to compute the similarity andprovide candidate items rather than some specific algorithm. Then they utilized additional features, such as item absolute likability, item relative likability, item rating
variance, and neighbors’ rating node to re-rank the candidate items. Finally selected
top N items for recommendations. This approach could provide recommendations
with good diversity, but it comes at the expense of accuracy.

3.3 Analysis and problem setting
Neighborhood selection is to determine which users’ rating information will be utilized to compute the prediction for an active user, in other words, it decides whowill be selected as neighborhood of the active user. In theory, every user could beselected as a neighbor. However, modern commercial RSs have vast customers, e.g.,
Amazon has billions of users, it is impractical to consider every user as a neighborwhen trying to maintain real-time performance. A subset of users must be selected
as neighborhood if RSs want to guarantee acceptable response time. Herlocker et al.
(Herlocker, Konstan, and Riedl, 2002) discussed the size of neighborhood in detail,and drew a conclusion that the size of neighborhood affects the performance of RSs
in a reasonably consistent manner. It suggests that, in the real-world situations, aneighborhood of 20 to 60 neighbors is reasonable to be used to make predictions.
Currently, k-NN method is often used in the traditional UBCF approach to makeneighborhood selection, neighborhood is comprised by the top k users with highest similarity in candidate neighbors. However, in RSs, some items, especially the
popular items, have high rating scores from most of users, and the active user usually also prefer these items. In this case, when using the traditional UBCF approach,users who prefer the popular items are likely to have high similarity with the active
user, so they will easily appear in the neighborhood. Other users, who prefer niche
items, are difficult to be selected as the neighborhood, but these niche items may also
be preferred by the active user. For example, the relevant items of user 1 and user 2
are popular items, the relevant items of user 3 are niche items. Similarity between
active user and them are 0.9, 0.8, and 0.7, respectively, besides that, user 2’s relevant
item set is included in user 1’s relevant item set. In traditional UBCF approach, if weselect two most similar users as neighborhood, user 1 and user 2 will be selected, in
this case, only popular items will be recommended to the active user. However, user
3 also have high similarity with the active user, relevant items of user 3 may also be
preferred by the active user. In order to obtain neighborhood with diverse tastes, we
can remove user 2 and select user 1 and user 3 as the neighborhood. Because therelevant item set of user 2 is included in user 1’s relevant item set, so we can only
utilize user 1 to make predictions for popular items rather than both of them. Here,we consider users like user 2, whose relevant item set is included in other user’s relevant item set, as the redundant users. In traditional UBCF approach, the k-nearest
neighbors have similar taste, so they tend to have similar relevant items, therefore
neighborhood usually contains many redundant users. When making prediction,
they tend to give high predicted scores for few types of items, even just the popular

14

Chapter 3. CBCF for active users’ personalized recommendations

items. It causes the traditional UBCF approach cannot provide recommendations
with good values of accuracy and diversity simultaneously.

3.4 CBCF for an active user’s personalized recommendations
3.4.1 Motivation of CBCF approach
The proposed CBCF approach aims to improve the traditional UBCF approach by reducing redundant users, and constructs neighborhood by users who have high similarity and diverse relevant items. As we discussed above, redundant user’s relevant
item set is included in other user’s relevant item set. According to discussions in
section 2.3, reducible element in the second type of covering reduction algorithm isalso included in other elements, so we can remove redundant users by using the second type of covering reduction algorithm. Removing all reducible elements means
we remove all redundant users.
In general RSs, there are vast items, it means the item domain I is too large. However, different users have rated different items, it may cause not so many users could
be considered as redundant users. In order to remove redundant users as many as
possible, item domain should be reduced as much as possible. In CBCF approach,
we reduce the domain from item set I to active user’s decision class D. Items fit theactive user’s relevant attributes comprise the decision class D. However, in practicalapplication, users usually do not enter their relevant attributes into RSs. Here, inorder to obtain relevant attributes of an active user, we sum each attribute value inthe active user’s relevant item set. Due to the more high rating scores indicate thatthe more the active user likes the attribute, l number attributes with the largest sums
are selected as the relevant attributes.
Relevant attributes of the active user in the following form:[at1 = av1 ] ∧ [at2 = av2 ] ∧ ... ∧ [atm = avm ],where m means the number of all attributes, atm is an attribute and avm is the valueof atm .

3.4.2 Procedures of CBCF approach
In CBCF approach, we insert user reduction step into the traditional UBCF approach.
Algorithm 3.1 presents concise steps of the CBCF approach. The detailed procedure
is as follows:
Step 1: User reduction. First set I as the domain, relevant items of each user
comprise a set in domain I. We construct decision class D for the active user au.
The decision class D consists of all items that fit the active user’s relevant attributes,
defined by (3.1).
D = {i ∈ I|at1 (i) = av1 , at2 (i) = av2 , ..., atm (i) = avm },(3.1)

where atm (i) = avm means that the value of the attribute atm on item i is avm .
Then to remove as many redundant users as possible, we reduce the domainfrom item set I to decision class D, and for each user u ∈ U , the relevant items ofuser u in domain D comprise the relevant set Cu , where
Cu = {i ∈ D|ru,i ≥ θ}.

(3.2)

3.4. CBCF for an active user’s personalized recommendations

15

Let C∗ = D − ∪Cu ; then, C = {C1 , C2 ...Cn , C ∗ } is a covering for the active user indomain D.
Next, based on the second type of covering reduction algorithm in the coveringbased rough sets, redundant elements are removed from covering C to obtain reduct(C),we can obtain the active user’s reduct-users U r , whereU r = {u ∈ U |Cu ∈ reduct(C)}.

(3.3)

Step 2: Similarity computation. Users in U r comprise candidate neighbors CN rau
of the active user au. According to the rating information, compute the similarity
sim(au, u) between the active user au and each user u ∈ CN rau by the similarity
measure.
r (k) isStep 3: Neighborhood selection. The active user au’s neighborhood Naurcomposed by k most similar (nearest) users in CN au .
r (k),
Step 4: Rating prediction. Based on rating information of neighborhood Nau
cwe predict rating score pau,i for each item i in unrated item set Iau of the active user
au.
Algorithm 3.1 CBCF approach
Input: User-item rating matrix RM , item attribute matrix AM , and an active userau.
Output: Recommended items set of size N for the active user au.
r (k) of the active user au.
k : Number of users in the neighborhood Nau
N : Number of items recommended to the active user au.
D : Decision class of the active user au.
U r : Users after making user reduction, reduct-users.
c : Items which have not yet rated by the active user au.
Iau
CN rau : Candidate neighbors of the active user au after making user reduction.
pau,i : Rating prediction of item i for the active user au.
1: for each user u ∈ U do
2:
Cu = {i ∈ D|ru,i ≥ θ}.
3: end for
4: Let C∗ = D − ∪Cu ; then, C = {C1 , C2 ...Cn , C ∗ } is a covering for an active user
au in domain D.
5: reduct(C) = ST CRA(C)
6: Reduct-user U r = {u ∈ U |Cu ∈ reduct(C)}.
7: CN rau = U r , compute similarity between the active user au and each user u ∈CN rauc do
8: for each item i ∈ Iau
r (k);
9:Find the k most similar users in CN rau to comprise neighborhood Naur (k);
10:
Predict rating score pau,i for item i by neighborhood Nau
11: end for
12: Recommend to the active user au the top N items having the highest pau,i .

3.4.3 Example of CBCF approach in RSs
Here, we present an example to explain the CBCF approach more clearly. Table 3.1
illustrates an User-Item rating matrix RM about rating scores by six users for eightitems, Uau represents the active user. The rating value is from 1 to 5, where a higher
value indicates that the user likes the given item more. Table 3.2 shows the item

Chapter 3. CBCF for active users’ personalized recommendations

16

attribute matrix AM about eight items, and each item has the following attributes:
Horror, Comedy, Drama, Action, and Musical, where a value of 1 indicates that theitem is of that genre and a value of 0 indicates it is not. Note that items can be inseveral attributes simultaneously. The detailed steps are as follow:
TABLE 3.1: Example of user-item rating matrix RM

User
U1
U2
U3
U4
U5
Uau

Item 121
1221Item 24322
44Co-rated items
Item 3 Item 4
1
3
2
3
3
5
5
1
5
2
2
5

Item 5323412

Target items
Item 7 Item 8
5
5
2
3
2
1
1
4
5
3
⋆
⋆

Item 645
4
533

TABLE 3.2: Example of item attribute matrix AM

Item
Item 1
Item 2Item 3Item 4Item 5Item 6Item 7Item 8

Horror
1
0
1
1
0
0
0
1

Comedy
0
1
1
1
1
1
1
0

Attribute
Drama Action
1
0
0
1
0
1
1
1
1
1
0
1
1
0
0
1

Musical
0
1
1
0
0
0
0
1

TABLE 3.3: Example of similarity and rank depending on different
approaches

User-User
Uau − U1
Uau − U2
Uau − U3
Uau − U4
Uau − U5

Traditional UBCF
Similarity Rank
0.501
3
0.563
2
0.646
1
-0.45850.075
4Proposed CBCF
Similarity Rank
0.501
2
0.646
1
0.075
3

Step 1: User reduction. Here, we treat the rating threshold θ as 3; thus, from therating matrix RM we can obtain the active user’s relevant items set {Item 2, Item
4, Item 6}. We sum each attribute value in the relevant item set according the item
attribute matrix AM (Horror=1, Comedy=3, Drama=1, Action=3, Musical=1). Then,
two attributes with the largest sums (Comedy and Action) are selected as relevant
attributes of the active user. Then, all items that fit the relevant attributes comprisethe decision class D = {Item 2, Item 3, Item 4, Item 5, Item 6}.
Reduce the domain from all items set to decision class D. Relevant items of theuser u in domain D will be a set Cu :C1 = {Item 2, Item 4, Item 5, Item 6}, C2 = {Item 2, Item 4, Item 6},
C3 = {Item 3, Item 4, Item 5, Item 6}, C4 = {Item 3, Item 5, Item 6},

3.4. CBCF for an active user’s personalized recommendations

17

C5 = {Item 2, Item 3, Item 6}.
Then, C = {C1 , C2 , C3 , C4 , C5 } is a covering for the active user in domain D. Basedon the definition of the second-type covering reduction algorithm, C2 ⊂ C1 , C4 ⊂ C3 ;thus, C2 and C4 can be regarded as redundant elements to be removed. Then, we
can obtain the reduct(C) = {C1 , C3 , C5 }, so the reduct-users U r = {U1 , U3 , U5 }.
r for the active user
Step 2: Similarity computation. Candidate neighbors CNaur = U r = {U , U , U }. Then utilize the Pearson
are composed by users in U r , CNau135correlation coefficient similarity measure to compute the similarity between the acr . Table 3.3 shows results of similarity and user rank
tive user and each user in CNaufor the traditional UBCF and proposed CBCF approaches.
Step 3: Neighborhood selection. If we consider only three nearest users in candidate neighbors as neighborhood of the active user, U1 , U2 , and U3 will comprise theneighborhood Nau (3) for the traditional UBCF; however, for our proposed CBCF
r (3).
approach, U1 , U3 , and U5 will be considered as the neighborhood Naur
Step 4: Rating prediction. From the rating scores of Nau (3), we use the adjusted
weighted sum approach to predict the rating scores for item 7 and item 8. HerePau,7 = 3.284, Pau,8 = 2.588
Because Pau,7 > Pau,8 , if we select the top one movie as recommendation, item 7 will
be recommended to the active user.

3.4.4 Discussion
To provide recommendations with good values of accuracy and diversity for an active user au, the biggest innovation of the proposed CBCF approach is that, we insert
the user reduction procedure into the traditional UBCF approach. For an active user
au, before computing the similarity, we remove redundant users from all users to
r with diverse
obtain reduct-users U r and which comprise candidate neighbors CNaurtastes, k most similar (nearest) users selected from CNau comprise neighborhood
r (k). Although comparing with input conditions of the traditional UBCF, ourNauproposed CBCF needs an additional condition: item attribute matrix AM ; however,in general RSs, item attribute matrix is very common and easy to obtain.
User reduction is a core component of CBCF approach, which applies the notionof covering reduction to reduct redundant users from all users. First, we set all items
I as the domain, and relevant items of each user comprise a set in domain I. However, in this case, there are only a few sets can be removed as redundant elements.
To remove as many redundant users as possible, when obtaining the decision classD, we reduce the domain from I to D such that the domain can be sufficiently small.
Then, the relevant items of each user in decision class D will be a element of a covering C. Based on the definition of the second type of covering reduction algorithm,
for set C1 , if there exists another set C2 for which C1 ⊂ C2 , C1 is considered reducible and therefore removable. In this approach, C1 denotes the relevant items ofuser 1 in domain D and C1 ⊂ C2 indicates that user 1 and user 2 are likely to prefersame type of items, so we can just utilize user 2 to make prediction for this type of
items, thus user 1 can be considered as redundant user to be removed. Removing
all reducible elements means that all redundant users are removed from all users,r . Users
so that this approach can only use the reduct-users U r to comprise CNaur have diverse relevant of items, and high similarity users are selected fromin CNaur (k). So in proposed CBCF approach, neighborsrCNau to comprise neighborhood Naur (k) have both high similarity and diverse preference, they can make acin the Nau
curate predictions for more types of items and present recommendations with highaccuracy and diversity at the same time.

18

Chapter 3. CBCF for active users’ personalized recommendations
TABLE 3.4: Average size of decision class versus l with the MovieLens
datasetl(number of relevant attributes)
Average size (decision class)1
583.2572
70.363

3
7.068

4
0.303

3.5 Experiments and evaluations
In this section, we introduce the evaluation dataset and metrics, examine the effectsof the approach components, and compare the CBCF approach’s performance withthe traditional UBCF approach with different datasets.

3.5.1 Experimental setup and evaluation metrics
In our experiments, we utilized the MovieLens (Herlocker et al., 1999) and Jester(Goldberg et al., 2001) datasets because they are often used to evaluate RSs. The
MovieLens 100K dataset consists of 1,682 movies, 943 users, and 100,000 ratings ona scale of 1 to 5. Each user has rated at least 20 movies, and in our study, movies rated
above 3 were treated as a user’s relevant movies. The Jester 3.9M dataset contains
ratings of 100 jokes from 24,983 users. Each user has rated 36 or more jokes. The
value range of rating scores is -10 to 10. A value of "99" represents an absent rating.
In our experiment, jokes rated above 5 were treated as a user’s relevant jokes.
We also used the conventional leave-one-out procedure to evaluate the performance of the proposed approach. For each test user, we only considered items thatthe user had rated as test items. First, we supposed that the test items had no ratingscores from the test user. Then, our approach predicted a rating score for each test
item using the information obtained from the remaining users. Finally, comparisonswere made between the original and predicted rating scores. For the MovieLens
dataset, we summed each attribute value in the test user’s set of relevant movies,
and l number attributes with the largest sums were selected as the relevant attributesof the test user. As there were 18 attributes for each movie, we computed the average
size of decision class in terms of different number of l. Table 3.4 shows the result.
If the size of test user’s decision class is too big, there will be just fewer redundantusers could be removed; however, if the size of test user’s decision class is too small,other users’ relevant item set will include this decision class easily, in this case, itwill lose the meaning of reduction. Overall consideration, we select two attributes
to construct the decision class.
For the Jester dataset, no information was presented about item attributes. There
were 100 jokes in this dataset, we considered the top 50 jokes sorted by the test
user’s rating scores as the decision class. If the number of rated jokes from the test
user was less than 50, we treated all rated jokes as the decision class. However if theneighbor’s set of relevant jokes was too large, it would include the decision class, inthis case, covering reduction will lose effectiveness. To avoid this, we selected the
top 10% users who had rated the fewest jokes from all users, and utilized these 2,498
users for our experiment.
To measure the performance of the proposed approach, we used the mean absolute error (MAE), root mean square error (RMSE) to represent the accuracy, andcoverage to evaluate the diversity of recommendations, all of which are popular
metrics for evaluating RSs.

3.5. Experiments and evaluations

19

The MAE and RMSE metrics demonstrate the average error between predictions
and real values; therefore, the lower these values, the better the accuracy of RSs.
M AE =∑∑
1
1(|pu,i − ru,i |),card(U )card(Ou )u∈U

RM SE =∑
1card(U )
u∈U

(3.4)

i∈Ou

√

∑
1
(pu,i − ru,i )2 ,card(Ou )

(3.5)

i∈Ou

where Ou = {i ∈ I|pu,i ̸= ⋆ ∧ ru,i ̸= ⋆} indicates set of items rated by user u having
prediction values.
In different research fields, the coverage metric can be interpreted and defineddifferently. We define coverage metric as calculating the percentage of situation inwhich at least one k-nearest neighbors of the active user can rate an item which has
not been rated by that active user. Here, let Su,i as the set of user u’s neighbors whichhave rated the item i, and define Zu = {i ∈ I|Su,i ̸= ∅}.
Coverage =∑1card(Iuc ∩ Zu )(100 ×).
card(U )card(Iuc )

(3.6)

u∈U

In addition, the reduction rate is defined as an evaluation metric, that measuresthe effectiveness of removing redundant users from all users. Reduction rate is given
as follows:∑ card(CN u − CN r )1uReductionRate =,(3.7)
card(U )card(CN u )u∈Uwhere CN u means candidate neighbors of user u, CN ru represents user u’s candidateneighbors after user reduction.

3.5.2 Experimental results and comparisons
We conducted experiments to demonstrate the performance of the proposed CBCF
approach. In addition, using different datasets, comparisons of the CBCF and traditional UBCF approaches were performed to verify if the proposed CBCF approach
could provide better recommendations or not than traditional UBCF approach. In
both experiments, the Pearson correlation coefficient approach was used as the similarity measure, k-NN approach was utilized to select the neighborhood, and theadjusted weighted sum approach was used as the aggregation function. To obtain
MAE, RMSR, and coverage values, according to (Herlocker, Konstan, and Riedl,
2002), we selected different size k neighborhood from candidate neighbors, k ∈{20, 25, 30, ..., 60}.
Currently, researches have gotten the conclusion that there is a trade-off relationship between accuracy and coverage in traditional UBCF approach. As increasing
the size of neighborhood, coverage metric increases constantly; however, for accuracy metric, it first increases and then decreases (Herlocker et al., 1999; Herlocker,
Konstan, and Riedl, 2002). In our experiments, due to the size of neighborhood isin a small range, experimental results may appear that both accuracy and coverage increase as the size of neighborhood increases. However, it does not negate thetrade-off relationship between accuracy and coverage in traditional UBCF approach.
Table 3.5 shows results about number of candidate neighbors for traditional UBCF
and CBCF approaches in MovieLens and Jester datasets respectively. As can be seen,in MovieLens dataset, there are 943 users, so in traditional UBCF approach, all 943

Chapter 3. CBCF for active users’ personalized recommendations

20

TABLE 3.5: Number of candidate neighbors for traditional UBCF andCBCF approaches

MovieLens
Jester

UBCF
943
2,498

CBCF
193
580

Reduction Rate
0.795
0.768

1.00

Accuracy Measure

0.50

Values of MAE and RMSE
0.60 0.70 0.80 0.90

CBCF(MAE)
CBCF(RMSE)UBCF(MAE)UBCF(RMSE)

15

20

25

30

35
40
45
50
Size of neighborhood

55

60

65

F IGURE 3.1: Accuracy results (MAE and RMSE) versus the size ofneighborhood with MovieLens dataset

users will be considered as candidate neighbors. After user reduction, on average,
approximately 79.5% of users are removed as redundant users, so in CBCF approach,remaining 193 users will comprise the candidate neighbors. In Jester dataset, recall
that there are 2,498 users, so the number of candidate neighbors for traditional UBCF
approach is 2,498. The reduction rate is 76.8%, which means approximately 76.8% ofusers are removed as redundant users on average, so in CBCF approach, the average
number of candidate neighbors is 580.
First, we introduce comparisons between the CBCF and traditional UBCF approaches with the MovieLens dataset. Figure 3.1 shows accuracy results (MAE and
RMSE) versus the size of neighborhood. As can be seen, for traditional UBCF approach, both MAE and RMSE values decrease as the size of neighborhood increases,
when the size of neighborhood is 60, they obtain the least values 0.626 and 0.801
respectively. On the other hand, for CBCF approach, the MAE and RMSE values
are stable, and values of two metrics are 0.623 and 0.788 when the size of neighborhood is 60. Overall, for MAE and RMSE metrics, all values of CBCF approach are
lower than traditional UBCF approach, which means that the predicted scores by
CBCF approach are closer to the original scores. So the proposed CBCF approach
outperforms traditional UBCF in terms of MAE and RMSE. Figure 3.2 illustrates the3.5. Experiments and evaluations

21

Values of coverage(100%)
20
40
60
80

100

Coverage Measure

0

CBCF
UBCF
15

20

25

30

35
40
45
50
Size of neighborhood

55

60

65

F IGURE 3.2: Coverage results versus the size of neighborhood with
MovieLens dataset

coverage metric versus the size of neighborhood. As shown in figure, the coverage of both CBCF and traditional UBCF approaches increases obviously as the sizeof neighborhood increases. However, the coverage of proposed CBCF approach ishigher than traditional UBCF in terms of different size of neighborhood, it means,
CBCF approach can recommend more types of movies that the active user has not
yet rated. Thus, the comparative results for CBCF and traditional UBCF obtainedwith MovieLens dataset indicate that, our proposed CBCF approach can select more
appropriate neighborhood, and outperform the traditional UBCF approach in termsof accuracy and coverage.
Next, we illustrate comparisons between the CBCF and traditional UBCF approaches with the Jester dataset. Figure 3.3 explains accuracy results (MAE and
RMSE) versus the size of neighborhood. As shown in the figure, for both CBCF andtraditional UBCF approach, values of MAE and RMSE increase slightly as the size ofneighborhood increases, it means the accuracy becomes lower when the neighborhood increases. And for MAE and RMSE metrics, all values of the proposed CBCF
approach are higher than traditional UBCF, it indicates that CBCF approach does notoutperform in terms of MAE and RMSE. Figure 3.4 shows the coverage metric versus the size of neighborhood. As can be seen, for both CBCF and traditional UBCF
approaches, coverage increases slightly as the size of neighborhood increases; however, traditional UBCF is lightly higher than the CBCF approach, which means the
CBCF approach cannot recommend more types of jokes for the active user. In conclusion, the comparative results between CBCF and UBCF with Jester dataset reveal
that, the proposed CBCF approach is inferior to the traditional UBCF approach interms of accuracy and coverage.

Chapter 3. CBCF for active users’ personalized recommendations

22

Values of MAE and RMSE
1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2

Accuracy Measure

CBCF(MAE)
CBCF(RMSE)UBCF(MAE)UBCF(RMSE)

15

20

25

30

35
40
45
50
Size of neighborhood

55

60

65

F IGURE 3.3: Accuracy results (MAE and RMSE) versus the size ofneighborhood with Jester dataset

Values of coverage(100%)
20
40
60
80

100

Coverage Measure

0

CBCF
UBCF
15

20

25

30

35
40
45
50
Size of neighborhood

55

60

F IGURE 3.4: Coverage results versus the size of neighborhood with
Jester dataset

65

3.6. Summary

23

3.5.3 Analysis and discussion
The experimental results indicate that the proposed CBCF approach demonstrates
different performance with different datasets. In the MovieLens dataset experiment,
there were 1,682 movies and 943 users. For each user, the number of rated items wasquite smaller than the number of unrated items; therefore, this dataset is very sparse.
In the proposed CBCF approach, user reduction procedure can remove redundantusers which may have high similarity but can only make predictions for few types of
items, reduct-users with diverse tastes comprise the candidate neighborhood, neighborhood selected from candidate neighbors can predict rating scores for more types
of items, so the coverage metric has improved greatly comparing with the traditional
UBCF approach. Furthermore, in RSs, although some users have higher similarity
with the active user, they cannot provide predictions with high accuracy. For example, some users have rated few items, they often also have few co-rated items withthe active user, even only one; however their rating scores for co-rated items are
similar. In this case, they will have high similarity, but they may not have similar
preferences with the active user, so they cannot provide predictions with high accuracy. As these users have fewer rated items, in CBCF approach, they are easy to beconsidered as redundant users to be removed, so accuracy metric of CBCF approach
has a great improvement than traditional UBCF approach.
In the Jester dataset experiment, we utilized 2,498 users; however, this dataset
has only 100 jokes. Thus, for each user, there are fewer unrated jokes than rated
jokes. Each joke may be rated many times by different users; thus, this dataset is notsparse. Under these circumstances, all jokes can be considered as popular jokes, andeach user can predict rating scores for sufficient types of jokes relative to all 100 jokes.
Due to co-rated items are sufficient between each two users, so users having higher
similarity with the active user can also provide predictions with higher accuracy.
In CBCF approach, user reduction procedure removes some redundant users withhigher similarity; however, these users can make predictions with higher accuracy,
so the accuracy metric decreases comparing with UBCF approach. Besides, as there
are only 100 jokes, and each user has rated sufficient jokes, it means each user canmake predictions for almost same types of jokes. So after user reduction, reductusers, which comprise candidate neighbors, may not have improvements to makepredictions for more types of jokes. Therefore, comparing with traditional UBCF
approach, the coverage metric of CBCF approach does not have improvements.
Generally, in practical applications, RSs must handle big data that include huge
numbers of users and items. Thus, for each user, only small number of items havebeen rated compared to the huge number of unrated items. Thus, most RSs havesparse datasets, such as the MovieLens dataset. However, for a sparse dataset, theproposed CBCF approach can select more appropriate neighborhood than the UBCF
approach and can make recommendations for the active user with satisfactory accuracy and coverage values simultaneously. Thus, the proposed CBCF approach has
important significance for RSs.

3.6 Summary
UBCF approach is the most commonly used and studied technology for making recommendations in RSs. Generally, we use accuracy and diversity to evaluate an RS;
however, although neighborhood selected by the traditional UBCF approach has
high similarity with the active user, neighborhood tends to have similar tastes, sothey are like to give high rating scores for few types of items, even only the popular

24

Chapter 3. CBCF for active users’ personalized recommendations

items. Therefore it is difficult for the traditional UBCF approach to provide satisfactory accuracy and coverage simultaneously.
In this chapter, we have presented the CBCF approach based on covering-basedrough sets to improve the traditional UBCF approach. In the proposed CBCF approach, we add the user reduction procedure into the traditional UBCF, covering
reduction in covering-based rough set is utilized to remove redundant users fromall users, users having diverse preferences comprise reduct-users. Neighborhood iscomposed by k most similar users in candidate neighbors which consist of reductusers, so that neighbors in the neighborhood not only have high similarity but also
have diverse tastes. Our experimental results indicate that, for sparse datasets (which
often appears in practical RSs), unlike traditional UBCF, the proposed CBCF approach can provide recommendations with good values of accuracy and diversity
simultaneously. Thus, the proposed CBCF approach can recommend satisfactory
recommendations and obtain high confidence from the active user.

25

Chapter 4

Improved CBCF for new users’
personalized recommendations
4.1 Introduction
CF approaches are popularly used in RSs owing to their satisfactory performance.
On the assumption that users who have similar preferences in the past will tend tohave similar tastes in the future, UBCF has been proposed and applied in practice(Herlocker et al., 1999). UBCF can provide satisfactory recommendations utilizing
only the user’s historical ratings, without requiring any other special information,and it has demonstrated remarkable success in RSs (Park et al., 2015; Kaleli, 2014;
Koohi and Kiani, 2016); however, for traditional UBCF, in candidate neighbors of atarget user, because there exists some redundant users who have higher similarity
but can make predictions only for a few types of items, the traditional UBCF usually cannot provide recommendations with satisfactory accuracy and diversity at
the same time (Herlocker, Konstan, and Riedl, 2002). Many studies have been conducted to increase the diversity of recommendations based on UBCF. Among thesestudies, some approaches can improve diversity significantly, but accompanied bylosses in accuracy (Adomavicius and Kwon, 2011; Adomavicius and Kwon, 2012).
Although some methods can improve accuracy and diversity simultaneously, theyrequire additional information that is often not available or incomplete (Gan and
Jiang, 2013; Niemann and Wolpers, 2013). CBCF is a useful approach, falling in thelatter research line mentioned above, that we proposed in our previous chapter toimprove UBCF by removing redundant candidate neighbors. However, all of thesestudies focus on providing satisfactory recommendations for an active user which
often has sufficient rating information; but a new user in RSs differs in some respects(e.g., number of ratings or rating score proportion), recommendation difficulty is increased (Lika, Kolomvatsos, and Hadjiefthymiades, 2014; Son, 2016; Bobadilla etal., 2012; Liu et al., 2014a). Therefore, researchers face the difficult problem of how
to utilize only easily obtained information to provide recommendations for a newuser with satisfactory accuracy and diversity simultaneously (Ahn, 2008; Tyagi and
Bharadwaj, 2012; Chen et al., 2013).
In this chapter, we aim to improve the previous CBCF to provide satisfactory
accuracy and diversity of recommendations simultaneously for a new user in RSs.
Because a new user often has few ratings, the previous CBCF cannot utilize the insufficient information to remove redundant candidate neighbors for a new user effectively. In our improved CBCF, in order to remove as many redundant candidateneighbors as possible for a new user, by analyzing the proportion and characteristic of new users’ rating scores, we reconstruct the decision class by the niche items
which have fewer ratings from users. In this way, different from the previous CBCF,

26

Chapter 4. Improved CBCF for new users’ personalized recommendations

our improved CBCF could remove redundant candidate neighbors efficiently without requiring any special additional information. Experimental results indicate that
our improved CBCF can not only improve the accuracy metric, but also increase thediversity of recommendations for a new user for the sort of sparse datasets that often
occur in connection with real RSs.
The remainder of this chapter is organized as follows. In Section 4.2, we introduce the traditional UBCF approach and review some studies that attempt to improve UBCF to obtain better performance. In Section 4.3, we present our problem
setting through an analysis of real-world datasets. In Section 4.4, we explain the
motivation and detailed procedures of our improved CBCF, then make comparisons
between the previous CBCF and improved CBCF. In Section 4.5, we describe our experiments and compare the results of our improved CBCF with other existing work.
Finally, in Section 4.6, we draw the summary of this chapter.

4.2 Related works
UBCF is an important approach popularly used in RSs that utilizes only a user’s historical ratings, without any special additional information; however, the traditional
UBCF cannot provide recommendations with satisfactory accuracy and diversity simultaneously (Herlocker, Konstan, and Riedl, 2002). In addition, recent research
has concluded that gains in RS diversity can frequently be accompanied by losses
in accuracy, making it difficult to select a reasonable trade-off between accuracy anddiversity (Liu, Shi, and Guo, 2012; Zhou et al., 2010). More detailed information andprocedures of UBCF could be found in Subsection 1.2.1.
To increase the diversity of recommendations while maintaining comparable levels of recommendation accuracy, we developed in our previous work a CBCF approach to improve the traditional UBCF. In previous CBCF, combining with the characteristics of redundant users in UBCF and redundant elements in covering-basedrough sets, we inserted a neighbor selection procedure into the traditional UBCF that
could remove redundant candidate neighbors by covering reduction algorithm. To
remove as many redundant users as possible, according to the sufficient informationfrom an active user, we first extracted relevant attributes of the active user, then constructed decision class by all items that fit the active user’s relevant attributes, andreduced the domain from all items to decision class. Because the CBCF approach
could select more appropriate users to comprise the neighborhood of an active user,
CBCF was able to provide recommendations with satisfactory accuracy and diversity simultaneously for an active user. More detailed information could be found in
Chapter 3.
Said, Jain, and Albayrak (Said, Jain, and Albayrak, 2012) investigated the effects
of weighting factors on different types of users. To improve the diversity of recommendations for a new user, when computing the similarity, they decreased theimpact of items rated by many users, in other words, popular items. In addition,
they increased the impact of items rated by few users. This approach can improvethe diversity of recommendations for a new user with acceptable accuracy; however,
it is unstable and performs differently for different selected datasets.

4.3 Analysis and problem setting
In this section, first, we analyze two popular datasets that are often used to evaluate
RS approaches. Then, in accordance with the analysis result, we discuss the problem

4.3. Analysis and problem setting

27

TABLE 4.1: Proportion of items and ratings in the MovieLens dataset

Ratings ≥ 10K
5K ≤ Ratings < 10K
1K ≤ Ratings < 5K
Ratings ≤ 1K

Item number
174
296
1,564
8,647

Item rate
1.63%
2.77%
14.64%
80.96%

Rating number
2,757,120
2,112,854
3,562,589
1,569,491

Rating rate
27.57%
21.12%
35.62%
15.69%TABLE 4.2: Proportion of items and ratings in the Netflix dataset

Ratings ≥ 50K
10K ≤ Ratings < 50K
1K ≤ Ratings < 10K
Ratings ≤ 1K

Item number
501
1,541
5,084
10,644Item rate
2.82%
8.67%
28.61%
59.90%

Rating number
45,020,066
34,889,199
17,193,080
1,569,491

Rating rate
45.63%
35.36%
17.42%
1.59%

setting of this chapter.

4.3.1 Data analysis
Here, we analyze two popular datasets that were collected from the real world
and are often used to evaluate RSs. One is the MovieLens 10M dataset (Herlocker
et al., 1999), obtained from the website of the GroupLens lab. This dataset contains 71,567 users, 10,681 movies, and a total of 10,002,054 ratings on a scale of{0.5, 1, 1.5, ..., 5}. Each user has rated at least 20 movies, resulting in a sparsity of
95.81%. The other is the Netflix dataset, obtained from the Netflix Prize website(http://www.netflixprize.com). This dataset contains a total of 100 million ratingsfrom 480,189 users over 17,770 movies (98.81% sparsity). The ratings are on a {1, 2,
3, 4, 5} scale, and each user has rated a different number of movies. In this chapter,considering the size of datasets and the number of experiments we conducted, weused the full datasets for analysis and smaller subsets for some of the experiments
in Section 4.5.
First, we perform statistical analyses for these two datasets. Tables 4.1 and 4.2
show the number of items and the corresponding ratings, as well as their proportions according to the different number of ratings. As shown in the tables, for theMovieLens dataset, items that have more than 5K ratings account for only 4.40%
of all items, but their corresponding ratings comprise 48.69% of all the ratings. In
the Netflix dataset, this performance is more obvious, even though items that havemore than 50K ratings comprise only a 2.82% proportion of all items, with ratingscorresponding entirely to them accounting for 45.63% of all ratings. From the data
analysis above, we can conclude that in real-world database, after sorting all items
by descending order according to the number of ratings, the top fewer items usuallycorrespond to a large proportion of the ratings. Therefore, in this chapter, we callthem popular items.
Next, we consider the proportion of rating scores on popular items. Figures 4.1
and 4.2 show the results. As found in the figures, in the MovieLens dataset, therating range is from 0.5 to 5 with half-star increments, but most of the rating scores
are concentrated on {3, 4, 5}. In the Netflix dataset, although the rating range isjust from 1 to 5, most of the rating scores are also included in {3, 4, 5}. These results indicate that users’ rating scores on popular items are relatively concentrated,with the difference between these rating scores not being very large. Because theChapter 4. Improved CBCF for new users’ personalized recommendations

1000000
6000000200000The number of ratings

1400000

28

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Rating Score

F IGURE 4.1: Proportion of rating scores on popular items in theMovieLens dataset

similarity computation is based on the rating scores of co-rated items, the closer the
rating scores, the more similar the two users. Therefore, if most of the co-rated items
between two users are popular items, then the rating scores between them will besimilar, and they will achieve higher similarity. From the above, we can conclude
that if co-rated items between two users concentrate on popular items, the similarity
between them will be higher.
Finally, we discuss the percentage of ratings on popular items by users with ratings no more than {20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200} in the two datasets.
Figures 4.3 and 4.4 show the results. As shown in the figures, the two datasets havethe same performance in that the percentage of ratings on popular items decreases as
the number of ratings by users increases. Users with having no more than 20 ratings
have the highest percentage, almost 74.72% in the MovieLens dataset and 72.74% in
the Netflix dataset. In general, new users often have fewer ratings (i.e., no more than
20 ratings). Therefore, we can conclude that most ratings of new users concentrateon popular items.

4.3. Analysis and problem setting

1.0e+075.0e+06
0.0e+00

The number of ratings

1.5e+07

29

1

2

3

4

5

Rating Score

F IGURE 4.2: Proportion of rating scores on popular items in the Netflix dataset

68.63

69.19

70

69.87

70.67

72

71.44

72.25

72.96

73.61

74

66.04

68

64.26

66

n=200

n=150

Users with no more than n ratings

n=100

n=90

n=80

n=70

n=60

n=50

n=40

n=30

64
n=20

Percentage of ratings on the popular items(%)

76

74.72

Chapter 4. Improved CBCF for new users’ personalized recommendations

30

F IGURE 4.3: Percentage of ratings on popular items by users with nomore than n ratings in the MovieLens dataset

4.3. Analysis and problem setting

31

72.45

72.69

n=150

Users with no more than n ratings

n=100

n=90

n=80

n=70

n=60

n=50

n=40

n=30

71.0

n=200

71.02

71.34

71.5

71.5

71.58

71.82

72.0

71.67

72.01

72.24

72.5

n=20

Percentage of ratings on the popular items(%)

72.74

73.0

F IGURE 4.4: Percentage of ratings on popular items by users with nomore than n ratings in the Netflix dataset

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Chapter 4. Improved CBCF for new users’ personalized recommendations

4.3.2

Problem setting

According to our conclusions obtained above, if co-rated items between two users
concentrate on popular items, then they will have higher similarity. Because mostratings of a new user are on popular items, if there are other users whose ratings also
concentrate on popular items, the similarity between them will be very high, andthese users can easily be selected into the neighborhood of the new user. Therefore,
in traditional UBCF, a new user’s neighborhood is usually comprised of users whose
ratings concentrate on popular items.
However, neighborhoods comprised of users whose ratings concentrate on popular items can make predictions only for fewer types of items, perhaps even only
popular items. Hence, in traditional UBCF, candidate items with high predictedscores are the most popular items, resulting in a low diversity of recommendations
for a new user. In addition, these users can predict accurate rating scores only for
popular items rather than for all types of items. Thus, the accuracy of recommendations for a new user will also be unsatisfactory. Here, we define users whose ratings
concentrate on popular items as redundant users of a new user. In the traditional
UBCF approach, as the neighborhood of a new user often contains many redundantusers, recommendations they produce might concentrate on popular items. A newuser’s acceptance of these recommendations will substantially increase the percentage of ratings on popular items and further improve the similarity between the newuser and redundant users. Under these circumstances, redundant users are easierto select into a neighborhood. Finally, as a consequence, a vicious circle is established, and a new user might be able to obtain only recommendations determinedby popular items.

4.4 Improved CBCF for a new user’s personalized recommendations
Here, we first discuss the motivation of our improved CBCF. Then, we introduce
the information about reconstruction of the decision class for a new user. Next, we
describe detailed process of improved CBCF, and make comparisons between it withthe previous CBCF.

4.4.1 Motivation of improved CBCF approach
In order to provide recommendations with satisfactory accuracy and diversity simultaneously for a new user, our improved CBCF aims to remove as many redundant users as possible, and utilizes the remaining more appropriate users to comprise the neighborhood of a new user.
The target of previous CBCF is to provide satisfactory recommendations for an
active user. Because an active user has rated many items, there is sufficient information that could be utilized. Therefore, in the previous CBCF, the decision classconsists of items that fit the active user’s relevant attributes, and relevant attributes
can be obtained from sufficient rating information. However, for a new user, ratings
are usually very few, and it is unreliable to extract relevant attributes according to a
new user’s rating information. Moreover, in the previous CBCF, the item attribute
matrix had to be inputted as the indispensable condition, even though some datasets
do not have this information. Therefore, for a new user’s personalized recommendations, in our improved CBCF approach, we must make full use of the characteristic

4.4. Improved CBCF for a new user’s personalized recommendations

33

of a new user (e.g., fewer ratings or ratings concentrating on popular items), and reconstruct the decision class while ensuring as far as possible that the new approachrequires no special additional information.

4.4.2 Reconstruction of decision class for a new user
In accordance with the discussion in Section 4.4.1, we reconstruct the decision classfor the new user as the set of niche items in the dataset used for recommendation.
As we discussed in Section 4.3.1, in real-world database, after sorting all items bydescending order according to the number of ratings, the top fewer items correspondto a large population of the ratings, we called them popular items. We then define
niche items as items that are not popular in the dataset.
There are the following two reasons why we reconstruct the decision class forthe new user as the set of niche items:
1. Redundant candidate neighbors for a new user are able to be removed as many
as possible;
2. The decision class as the set of niche items is easily constructed from the useritem matrix.
The first reason is that we can remove redundant candidate neighbors for a newuser as many as possible. It is because that in candidate neighbors of a new user,
items rated by a redundant user U1 are the most popular items, with the result thatthe set of niche items rated by U1 is very small, perhaps even empty. Under thesecircumstances, it is very easy to find another user U2 whose rated niche items’ set
includes U1 ’s. In other words, in the entire set of niche items, U2 can not only makepredictions for items as U1 does, but also predict ratings for other types of niche
items. Therefore, U2 might be more appropriate for being selected into the neighborhood than U1 , even though the similarity of U1 might be a little higher. When
reducing the domain from item set I to the decision class comprised by the set ofniche items, some redundant users who have not rated niche items will be removed
first. Then because of a redundant element in a covering is also included in otherelements, which has the same characteristics as the set of items rated by redundantusers, we can utilize covering reduction to remove redundant users. Even using
the covering reduction algorithm cannot remove all the redundant users, but it canremove most of them, and our experiments in Section 4.5 confirm this.
The second reason is that, by utilizing the set of niche items, decision class canbe constructed easily without requiring any other special additional information. It
is because that the niche items could be extracted easily from the user-item rating
matrix which can be obtained from almost all types of RSs.
Algorithm 4.1 constructs a decision class Dnu for new user from the user-item
rating matrix RM and the ratio threshold rt (0 < rt < 1). In this algorithm, the setof popular items is regarded as the top (1 − rt) × 100% items which have the largest
number of ratings in I and the decision class Dnu for new user is constructed byremoving popular items from the set of all items I.

4.4.3 Procedures of improved CBCF approach
In this subsection, we describe the detailed steps of the improved CBCF approachand provide algorithm information in Algorithm 4.2.

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Chapter 4. Improved CBCF for new users’ personalized recommendations

Algorithm 4.1 Decision class construction algorithm for new user
Input: User-item rating matrix RM , ratio threshold rt
Output: Decision class Dnu for new user
1: for all i ∈ I do
2:ni ← Count the number of users u ∈ U such that ru,i ̸= ⋆3: end for
4: Dnu = I|Dnu |
5: while |I|≥ rt do
6:i ← Select an item with highest value ni in Dnu
7:
Dnu ← Dnu \ {i}8: end while
9: return Dnu
Step 1: User reduction. First set I as the domain, with rated items of each user
comprising a set in domain I. We construct a decision class Dnu for a new user nu,
consisting of all niche items. Then to remove as many redundant users as possible,
we reduce the domain from item set I to decision class Dnu , and for each user u ∈ U ,user u’s rated items Iu in domain Dnu comprise the set Cu , where
Cu = Iu ∩ Dnu .

(4.1)

Let C∗ = Dnu − ∪Cu . Then, C = {C1 , C2 ...C|U | , C ∗ } − {∅} is a covering for the
new user in domain Dnu . Next, based on the covering reduction algorithm forcovering-based rough sets, redundant elements are removed from covering C toobtain reduct(C). We can obtain a new user’s reduct-users U r , whereU r = {u ∈ U |Cu ∈ reduct(C)}.

(4.2)

Step 2: Similarity computation. Users in U r comprise candidate neighbors CN rnu
of the new user nu. According to the historical rating information, compute thesimilarity sim(nu, u) between the new user nu and each user u ∈ CN rnu according
to the similarity measure.
r (k) conStep 3: Neighborhood selection. The new user nu’s neighborhood Nnu
rsists of the k most similar (nearest) users in CN nu .
Step 4: Rating prediction. Based on the rating information of neighborhood
r (k), we predict the rating score pcNnu
nu,i for each item i in unrated item set Inu of
the new user nu.
Step 5: Item recommendation. According to the predicted rating scores, selectthe top N items that have the highest pnu,i from the candidate items as the recommendations for the new user nu.

4.4.4 Comparisons between the previous CBCF and improved CBCF
As the same to the previous CBCF, our improved CBCF also has inserted a neighbor selection procedure into the traditional UBCF. However, because the target user
is different, neighbor selection methods for two approaches are also different. For
the previous CBCF, it aims to provide satisfactory recommendations for an active
user which often has many ratings. In order to improve the quality of recommendations, in previous CBCF, we construct the decision class through the sufficient
rating information obtained from the active user, and the input information needs
both user-item rating matrix and item attribute matrix. Different from the previous

4.5. Experiments and evaluations

35

Algorithm 4.2 Improved CBCF approach
Input: User-item rating matrix RM and a new user nu.
Output: Recommended items set of size N for the new user nu.
r (k) of the new user nu.
k : Number of users in the neighborhood Nnu
N : Number of items recommended to the new user nu.
Dnu : Decision class of the new user nu.
U r : Users after user reduction, reduct-users.
c : Items that have not yet been rated by the new user nu.
Inu
CN rnu : Candidate neighbors of the new user nu after user reduction.
pnu,i : Rating prediction of item i for the new user nu.
1: for each user u ∈ U do
2:
Cu = Iu ∩ Dnu .
3: end for
4: Let C∗ = Dnu − ∪Cu . Then, C = {C1 , C2 ...C|U | , C ∗ } − {∅} is a covering for the
new user nu in domain Dnu .
5: reduct(C) = ST CRA(C)
6: Reduct-user U r = {u ∈ U |Cu ∈ reduct(C)}.
7: CN rnu = U r , compute the similarity between the new user nu and each useru ∈ CN rnu
c do
8: for each item i ∈ Inur (k);
9:Find the k most similar users in CN rnu to comprise neighborhood Nnu
r (k);
10:
Predict rating score pnu,i for item i by neighborhood Nnu
11: end for
12: Recommend to the new user nu the top N items having the highest pnu,i .

CBCF, for our improved CBCF, the target user is a new user, we construct the decision class by niche items which could be extracted easily from the user-item rating
matrix. Moreover, our improved CBCF only needs to input the user-item rating matrix rather than any other special information from a new user. Through the above
comparisons, we can find that, to provide personalized recommendations, our improved CBCF needs less information (e.g., input information and the target user’s
ratings information) than previous CBCF. Because a new user usually has insufficient information which could be utilized, so our improved CBCF is more suitable
for the new user’s personalized recommendations.

4.5 Experiments and evaluations
In this section, we introduce the evaluation dataset and metrics, and compare theperformance of the improved CBCF approach with other work using different datasets.

4.5.1 Experimental setup and evaluation metrics
In our experiments, we used the MovieLens and Netflix datasets to evaluate our
improved CBCF approach. We also used the Jester dataset (Goldberg et al., 2001),
because it has characteristics different from the former two datasets. This dataset
contains ratings of 100 jokes from 24,983 users (27.53% sparsity). From the information of these three datasets, we find that MovieLens and Netflix are the same typeof dataset, each containing a huge number of items; however, each user has rated
fewer items, with the number of rated items being substantially smaller than the

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Chapter 4. Improved CBCF for new users’ personalized recommendations

number of unrated items. Therefore, the two datasets are very sparse, and popular
items can be found easily. In contrast, the Jester dataset contains only 100 items, eachuser has rated a sufficient number of items relative to all items, and unrated items
are fewer than rated ones; hence, this dataset is not sparse. In addition, in the Jester
dataset, every item has been rated by many users, with the result that it is difficult
to distinguish whether an item is popular. In fact, we can even say that each item ispopular.
To obtain a dataset of manageable size in our experiment, for the MovieLens
and Netflix datasets, we select 1,000 users and 1,000 items from each of the original
datasets. First, we select 1,000 items based on the item and rating structure of theoriginal dataset by stratified sampling (detailed information is given in Tables 4.3
and 4.4), and we set the ratio threshold rt = 0.95, it means the top 5% of items
that have the most ratings as popular items, with the remaining 95% items being
considered as niche items in our experiments. Next, because we only select 1,000
experimental users from the whole users (71,567 users in the MovieLens dataset,
480,189 users in the Netflix dataset), the sample size is very small relative to theoriginal data. In order to ensure experimental users as similar with the original
datasets as possible, we extract users that satisfy the following two conditions ascandidate users:
1. The user has at least one rated item in the selected 1,000 items;
2. Percentage of popular items in the user’s rated items is no less than the minimum value showed in Figures 4.3 and 4.4.
For example, in the case of the MovieLens dataset, a user is extracted as a candidateuser if the user has at least one item in the selected 1,000 items and the percentage
of popular items in this user’s rated items is no less than 64.26%. Similarly, for the
Netflix dataset, the percentage of popular items in a candidate user’s rated items is
no less than 71.02%. In this way, users who have quite different rating proportion
with original datasets will not be extracted, so that every experimental user selectedfrom candidate users could have a rating proportion as closely with the original
datasets as possible. Then, we select 200 test users and 800 training users from thecandidate users. First, we randomly select 200 users who have ratings numbering
no less than five and no more than 25 as the test users, and randomly mask 20% of
the ratings in each test user. We regard every test user as a new user, and each newuser has at most 20 ratings as training ratings by the masking of ratings and at most
5 ratings as test ratings in our experiments. Finally, 800 users are randomly selectedfrom candidate users as the training users.
In contrast, for the Jester dataset, because there are only 100 items, we treat the
top 50 items that have the most ratings as popular items and the remaining items as
niche items in the experiments. Since each user has rated 36 or more jokes in Jester
dataset, here we randomly select 200 test users and remove some of their ratings
to make them as new users, and 800 users are selected randomly as training users.
To avoid the impact of accidental phenomena, we repeat the experiments 20 times
for each dataset and compute the average values as our results. After selecting our
experimental items and users from original datasets, the average sparsities of selected datasets from MovieLens, Netflix, and Jester are 98.90%, 88.83%, and 36.42%,
respectively. Although the sparsities are a little different with original datasets, we
can also call selected datasets from the MovieLens and Netflix are sparse, and selected dataset from the Jester is not sparse.

4.5. Experiments and evaluations

37

TABLE 4.3: Experimental items versus original data in the MovieLens
datasetExperimental items
16
28
146
810
1,000

Ratings ≥ 10K
5K ≤ Ratings < 10K
1K ≤ Ratings < 5K
Ratings ≤ 1K

Original items
174
296
1,564
8,647
10,681

Item rate
1.63%
2.77%
14.64%
80.96%100%

TABLE 4.4: Experimental items versus original data in the Netflix
datasetRatings ≥ 50K
10K ≤ Ratings < 50K
1K ≤ Ratings < 10K
Ratings ≤ 1K

Experimental items
28
87
286
599
1,000

Original items
501
1,541
5,084
10,644
17,770Item rate
2.82%
8.67%
28.61%
59.90%
100%

To measure the performance of the improved CBCF approach, we used the mean
absolute error (MAE) and root mean square error (RMSE) to represent the accuracy of recommendations. In addition, we used coverage, mean personality (MP),and mean novelty (MN) to evaluate the diversity of recommendations. In accordance with Herlocker’s research (Herlocker, Konstan, and Riedl, 2002), to maintainreal-time performance, we selected different sized k neighborhoods from candidateneighbors, k ∈ {20, 25, 30, ..., 60}. The detailed information of MAE, RMSE and coverage metrics can be found in Subsection 3.5.1.
MP indicates the average degree of overlap between every two users’ recommendations. For example, for two users ui and uj , we count the number of recommendations of the corresponding top N items, Reci (N ) and Recj (N ), and further
normalize this number by the threshold value N to obtain the degree of overlap
between two sets of recommendations. It is clear that an approach of higher recommendation diversity will have a larger MP. As discussed by Gan and Jiang (Gan and
Jiang, 2013), we use N = 20 in our calculation of this metric.
M P (N ) = 1 −1
2
N |U |(|U | − 1)∑

|Reci (N ) ∩ Recj (N )|.

(4.3)

1≤i<j≤|U |MN indicates the novelty of recommendations provided to users. First, it calculates the fraction of users who have ever rated each recommendation, and thencomputes the sum over all recommendations in Recm (N ) to obtain the novelty foruser um . Finally, we calculate the average novelty over all users.
M N (N ) = −

1
|U |

∑

∑

log2 fn ,(4.4)

1≤m≤|U | n∈Recm (N )where fn indicates the fraction of users who rated the nth item. We also set N = 20 in
the calculation of this metric, and an approach will have a larger MN if it can makenewer recommendations.

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Chapter 4. Improved CBCF for new users’ personalized recommendations
TABLE 4.5: Number of candidate neighbors for the traditional UBCF
and CBCF approaches

MovieLens
Netflix
Jester

UBCF
800
800
800

CBCF
331
369
588

Reduction rate
0.586
0.539
0.265

In addition, the reduction rate is also used to measure the effectiveness of removing redundant users from among all users. The definition of reduction rate can befound in Equation 3.7.

4.5.2 Experimental results and comparisons
To show the performance of the improved CBCF, we compared it with traditional
UBCF. Comparisons were also made with the linear collaborative filtering (LINCF)
and inverse user frequency collaborative filtering (IUFCF) presented by Said et al.
(Said, Jain, and Albayrak, 2012). For convenience, we refer to the improved CBCF
approach as CBCF in the rest of this section. In all of the experiments, we used
the Pearson correlation coefficient as the similarity measure, and the weighted sum
approach to predict the rating scores. Finally, we selected the top N candidate objects
with the highest predicted rating scores as the recommendations for a new user.
Table 4.5 shows the results for the number of candidate neighbors for the traditional UBCF and CBCF approaches on the MovieLens, Netflix, and Jester datasets.
As can be seen, in the MovieLens and Netflix datasets, after user reduction, on average, more than half of the users are removed as redundant users, with the resultthat in the CBCF approach, fewer than half of the users will remain to comprise thecandidate neighbors. In the Jester dataset, the reduction rate is slightly lower, whichmeans that approximately 26.5% of the users are removed as redundant users on average, with the result that in the CBCF approach, the average number of candidateneighbors is 588.
Figures 4.5, 4.6, 4.7, and 4.8 show the results for MAE and RMSE on the MovieLens and Netflix datasets. As shown in the figures, with increasing neighborhoodsize, both the MAE and RMSE values decrease in the two datasets. In the MovieLens
dataset, both the MAE and RMSE values of the UBCF approach are higher than in
the other three approaches, because the lower these values, the better the accuracy,
indicating that the other three approaches have improved the accuracy of the traditional UBCF approach. Furthermore, although the MAE and RMSE values of CBCF
are higher than those of IUFCF and LINCF in the beginning, CBCF decreases fasterthan the other approaches as the neighborhood size increases. This indicates that the
CBCF approach can provide recommendations with higher accuracy than the otherapproaches as the neighborhoods grow. In the Netflix dataset, the MAE and RMSE
values of UBCF are lower than those of LINCF and IUFCF, indicating that the accuracy of UBCF outperforms that of LINCF and IUFCF; however, the values of UBCF
are also higher than those of CBCF, demonstrating that CBCF has improved the accuracy of traditional UBCF, and can provide recommendations with better accuracy
than the other approaches.
In contrast, these approaches have different performances on the Jester dataset.
As can be seen in Figures 4.9 and 4.10. MAE and RMSE of UBCF have the lowest
values, indicating that the accuracy of UBCF is highest among these approaches.
This indicates that the CBCF approach and other related work cannot improve the4.5. Experiments and evaluations

39

0.88

0.90

MAE Measure

0.86

●

●
●

Values of MAE
0.80 0.82 0.84

●
●
●
●
●

0.76

0.78

●

0.72

0.74

●

15

UBCF
CBCF
IUFCF
LINCF
20

25

30

35
40
45
Size of neighborhood

50

55

F IGURE 4.5: Result of MAE measure on the MovieLens dataset

60

65

Chapter 4. Improved CBCF for new users’ personalized recommendations

40

0.80

0.82

MAE Measure

Values of MAE
0.76
0.78

●
●
●
●
●
●

●

●

50

55

60

0.72

0.74

●

0.70

●

15

UBCF
CBCF
IUFCF
LINCF
20

25

30

35
40
45
Size of neighborhood

F IGURE 4.6: Result of MAE measure on the Netflix dataset

65

4.5. Experiments and evaluations

41

RMSE Measure
0.94

●

0.92

●

●

●
●

Values of RMSE
0.86
0.88
0.90

●
●
●

0.82

0.84

●

0.80

●

15

UBCF
CBCF
IUFCF
LINCF
20

25

30

35
40
45
Size of neighborhood

50

55

60

F IGURE 4.7: Result of RMSE measure on the MovieLens dataset

65

Chapter 4. Improved CBCF for new users’ personalized recommendations

42

0.88

0.89

0.90

RMSE Measure

Values of RMSE
0.86
0.87

●

●

●

0.85

●
●

0.84

●

0.83

●

15

●

●

●

50

55

60

UBCF
CBCF
IUFCF
LINCF
20

25

30

35
40
45
Size of neighborhood

F IGURE 4.8: Result of RMSE measure on the Netflix dataset

65

4.5. Experiments and evaluations

43

4.40

MAE Measure
UBCF
CBCF
IUFCF
LINCF

4.10

Values of MAE
4.15
4.20
4.25

4.30

4.35

●

●

●

●

●

●

●

●

●

50

55

60

4.00

4.05

●

15

20

25

30

35
40
45
Size of neighborhood

65

F IGURE 4.9: Result of MAE measure on the Jester dataset

accuracy of the UBCF approach. The MovieLens and Netflix datasets are sparse, but
the Jester dataset is not sparse, indicating that in sparse datasets, the accuracy of the
CBCF approach outperforms that of the other approaches. In non-sparse datasets,
the CBCF cannot improve the accuracy of the traditional UBCF approach.
Figures 4.11, 4.12, and 4.13 show the results for coverage on the MovieLens, Netflix, and Jester datasets, respectively. As shown in the figures, the values of the coverage metrics for all approaches increase as the neighborhood size increases. Furthermore, the coverage of CBCF is significantly higher than that of the other approaches,
especially for the MovieLens and Netflix datasets. This shows that CBCF can recommend more types of items that a new user has not rated yet. Therefore, we canconclude that CBCF improves the coverage of traditional UBCF and outperforms theother approaches for both sparse and non-sparse datasets.
Figures 4.14, 4.15, and 4.16 show the results for MP on the MovieLens, Netflix,
and Jester datasets, respectively. As shown in the figures, in both the MovieLens
and Netflix datasets, the MP values increase as the neighborhood size increases.
The IUFCF and LINCF approaches have improved the MP of the traditional UBCF
slightly; on the other hand, CBCF greatly increases the MP. In contrast, for the Jester

Chapter 4. Improved CBCF for new users’ personalized recommendations

44

4.80

RMSE Measure
UBCF
CBCF
IUFCF
LINCF

Values of RMSE
4.55
4.60
4.65

4.70

4.75

●

●

●

4.50

●

●
●

●

●

●

50

55

60

4.40

4.45

●

15

20

25

30

35
40
45
Size of neighborhood

F IGURE 4.10: Result of RMSE measure on the Jester dataset

65

4.5. Experiments and evaluations

45

Values of Coverage
10
12

14

16

18

Coverage Measure

●
●
●
●
●

8

●
●
●
●

UBCF
CBCF
IUFCF
LINCF

4

6

●

15

20

25

30

35
40
45
Size of neighborhood

50

55

60

F IGURE 4.11: Result of coverage measure on the MovieLens dataset

65

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46

Values of Coverage
9
10
11

12

13

14

15

Coverage Measure

●
●
●
●

8

●
●

7

●

6

UBCF
CBCF
IUFCF
LINCF

●

●

5

●

15

20

25

30

35
40
45
Size of neighborhood

50

55

60

F IGURE 4.12: Result of coverage measure on the Netflix dataset

65

4.5. Experiments and evaluations

47

94

96

Coverage Measure

●
●
●

92

●

Values of Coverage
88
90

●

●

●

86

●

84

●

82

●

UBCF
CBCF
IUFCF
LINCF

15

20

25

30

35
40
45
Size of neighborhood

50

55

F IGURE 4.13: Result of coverage measure on the Jester dataset

60

65

Chapter 4. Improved CBCF for new users’ personalized recommendations

48

0.78

0.80

MP Measure

0.76

●
●

Values of MP
0.74

●
●

●

0.72

●

0.70

●

●
●

0.68

●

15

20

25

30

35
40
45
Size of neighborhood

50

55

UBCF
CBCF
IUFCF
LINCF
60

65

F IGURE 4.14: Result of MP measure on the MovieLens dataset

dataset, the MP decreases as the neighborhood size increases, and the MP of traditional UBCF has the highest values. This indicates that CBCF cannot provide morediverse recommendations than UBCF. From the above results, we can conclude that
CBCF outperforms the other approaches on sparse datasets; nevertheless, it cannot
improve the diversity of the traditional UBCF approach on non-sparse datasets.
Figures 4.17, 4.18, and 4.19 show the results for MN on the MovieLens, Netflix,
and Jester datasets respectively. As shown in the figures, for both the MovieLens
and Netflix datasets, the MN values for UBCF, IUFCF, and LINCF are nearly thesame; however, CBCF obviously has higher MN values than the other approaches,
showing that CBCF can improve MN significantly. On the other hand, for the Jester
dataset, although the MN of CBCF is higher than that of UBCF at first, it decreases
faster as the neighborhood size increases, indicating that CBCF cannot provide recommendations with higher diversity than traditional UBCF. Therefore, we can conclude that in sparse datasets, CBCF can improve diversity more efficiently than theother approaches; however, it has poor performance for diversity as the neighborhood size increases in non-sparse datasets.

4.5. Experiments and evaluations

49

Values of MP
0.64 0.65 0.66 0.67 0.68 0.69 0.70 0.71 0.72 0.73 0.74 0.75

MP Measure

●
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●

●

15

20

25

30

35
40
45
Size of neighborhood

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F IGURE 4.15: Result of MP measure on the Netflix dataset

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Chapter 4. Improved CBCF for new users’ personalized recommendations

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4.5. Experiments and evaluations

51

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Chapter 4. Improved CBCF for new users’ personalized recommendations

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54

Chapter 4. Improved CBCF for new users’ personalized recommendations

4.5.3

Analysis and discussion

Our experimental results indicate that the improved CBCF approach shows different
performances with different datasets. For the MovieLens and Netflix datasets, there
are huge numbers of items; however, for each user, the number of rated items is
substantially smaller than the number of unrated items. Therefore, the two datasetsare very sparse. Under these circumstances, because of users’ different behaviors,
there might exist some users whose ratings concentrate only on popular items (users
whom we have defined as redundant users). As we have confirmed that a newuser’s ratings also concentrate on popular items, the similarity between redundantusers and the new user might be very high, and the neighborhood of the new usermight consist almost entirely of redundant users, causing recommendations fromthese redundant users to concentrate on fewer types of items, perhaps even only
popular items. The improved CBCF approach used the covering reduction algorithm to remove as many as these redundant users as possible. After user reduction,
the neighborhood of a new user in the improved CBCF approach consist of users
who have rated diverse items, with the result that the diversity of improved CBCF
greatly outperforms that of the other existing approaches. In addition, because ratings of redundant users concentrate on popular items, they have no ability to makeaccurate predictions for niche items, resulting in lower recommendation accuracy inthe traditional UBCF approach. In the improved CBCF approach, redundant users
in a neighborhood are mostly removed, with the result that a neighborhood in the
improved CBCF approach can make predictions for many types of items rather than
only popular items, thereby also improving accuracy.
In contrast, for the Jester dataset, the total number of items is 100, and each user
has rated at least 36 items. Hence, each user has rated sufficiently many items relative to all 100 items, and each item can be considered as a popular item. Therefore,
this dataset is not sparse. In this case, in the improved CBCF approach, each user
can be considered as a redundant user, with the result that reduction loses its significance. Recommendations from improved CBCF might also concentrate on popular
items, with the result that the diversity of the improved CBCF approach are inferior
to those of traditional UBCF. However, because user reduction can select users who
have rated more types of items, the coverage of improved CBCF is still higher thanthat of the other approaches. In addition, co-rated items between two users are sufficient, so neighbors with higher similarity can ensure the prediction of more accurate
ratings; however, some neighbors with higher similarity might be considered as redundant users to be removed, with the result that the accuracy does not improve but
decreases.
Generally, in practical applications, RSs must handle large data that include huge
numbers of users and items. Thus, for each user, only a small number of items havebeen rated compared with the huge number of unrated items. Therefore, most RSs
have sparse datasets, such as the MovieLens and Netflix datasets. However, for asparse dataset, the improved CBCF approach can remove redundant users to create
more appropriate neighborhoods than the UBCF approach and provide recommendations for a new user with more satisfactory accuracy and diversity values than inexisting work. Thus, the improved CBCF approach is significant for RSs.

4.6. Summary55

4.6 Summary
In this chapter, we have improved CBCF to achieve personalized recommendations
for a new user. The improved CBCF approach reconstructs the decision class to account for the set of niche items and uses covering reduction in covering-based roughsets to remove redundant users from candidate neighbors. By removing redundantusers who have high similarity with the new user but can make predictions for only
a few types of items, improved CBCF makes great improvements in both the accuracy and diversity metrics while utilizing only the user-item rating matrix with noother special information. Our experiments also show superiority of our approachby comparing it with traditional UBCF and other existing work. Although the improved CBCF is inferior to the traditional UBCF in non-sparse datasets (e.g., theJester dataset), it greatly outperforms other relevant work in sparse datasets (e.g., theMovieLens and Netflix datasets), which occur more often in the real world. Therefore, our approach could be applied to provide satisfactory recommendations for a
new user in real world RSs.

57

Chapter 5

Item-variance weighting for IBCF
by using time-related correlation
degree and covering degree
5.1 Introduction
RSs estimate ratings of items that are not yet to be consumed by users, and provide
items with the best predicted ratings for customers, so the accuracy is a key issue,
good or bad of accuracy directly affects service quality of RSs and user experience(Bobadilla et al., 2013). CF approaches are popularly used in RSs due to the satisfactory performances (Symeonidis et al., 2008). With the assumption that an item
will be preferred by a user if this item is similar with the preference of this user inthe past, IBCF has been proposed and has achieved success both in research andpractice in recent years, such as Amazon (Sarwar et al., 2001; Linden, Smith, andYork, 2003; Li et al., 2016; Li et al., 2014). In the traditional IBCF approach, all items
carry the same weight when computing the similarity and prediction; however, it is
widely recognized that some items are more important than others and should begiven relatively higher weighting (Karypis, 2001). So the traditional IBCF often can
not provide recommendations with satisfactory accuracy.
In this chapter, we reconsider the item weight in IBCF approach. We apply thetime weight to the item-item similarity computation to improve the predictive accuracy, ratings which rated by the same user in closer time will have higher weight
when computing the similarity. On the other hand, we insert the covering degree
to the rating prediction to increase the classification accuracy, items which are closer
with user’s preference will have higher covering degree, and will have high weight
when make prediction. Experiments prove that our proposed approach supply better performance than traditional IBCF and other existing work, and provide recommendations with satisfactory accuracy.
The remainder of this chapter is organized as follows. In Section 5.2, we introduce the traditional IBCF approach and related work. In Section 5.3, we analysis the
traditional IBCF and give the problem setting. In Section 5.4, we describe the timerelated correlation degree and covering degree, and apply them to the traditional
IBCF approach. In Section 5.5, we present our experiments and compare our resultswith the traditional IBCF approach and other work. Finally, in Section 5.6, we show
the summary of this work.

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Chapter 5. Item-variance weighting for IBCF by using time-related correlation
degree and covering degree

5.2 Related works
IBCF was first presented by Sarwar (Sarwar et al., 2001), which computes similarity between two items by comparing users’ ratings on them. IBCF can dramatically
improve the scalability of CF and can be applied to the huge numbers of items andusers that are typical of modern RSs. IBCF can easily handle large data sets andproduce better predictions than UBCF. Also in contrast to UBCF, IBCF is able tocompute item-item similarity off-line, both saving on-line time and making more effective recommendations (Papagelis and Plexousakis, 2005). Up until now, IBCF has
been widely used in many applications in the real world, such as at Amazon.com.
The detailed information and procedures could be found in Subsection 1.2.2.
Currently, many researchers have applied item-variance weighting to the traditional IBCF (Kefalas and Manolopoulos, 2017; Frémal and Lecron, 2017; Esparza,
OMahony, and Smyth, 2011; Hu and Ester, 2013; Koren, 2010; Lee, Park, and Park,
2008; Liu et al., 2014b). Ding and Li (Ding and Li, 2005) used clustering to discriminate between different kinds of items, and presented a novel algorithm to computethe time weights for different items in a manner that will assign a decreasing weightto old data. To each item cluster, they trace each user’s purchase interest change andintroduce a personalized decay factor according to the user own purchase behavior. Their new algorithm can substantially improve the precision of IBCF without
introducing higher order computational complexity. However, this approach only
considered the time weight to predict ratings, moreover, it did not consider otheritem weight that may make important effects on the performance of RS.

5.3 Analysis and problem setting
IBCF has achieved success both in research and practice. Similarity computation andrating prediction are two main procedures in IBCF. However, in traditional IBCF approaches, all items carry the same weight in both item-item similarity computation
and rating prediction. Generally, in our real life, it is widely recognized that someitems are more important than others and should be given relatively higher weighting. For example, as shown in Table 5.1, item 1, item 2, and item 3 are rated respectively by users: U1 , U2 , U3 for the same scores. If computing the similarity by the
traditional IBCF, similarity between item 1 and item 2 will be equal to the similarity
between item 1 and item 3. However, the rated time is different, because some people’s interests change with time quickly, an item that was rated recently by a sameuser should have a bigger impact than the item that was rated during a long timeinterval. Therefore, item 1 should be more similar with item 2 than item 3. Throughthe analysis of the above, we can find that, we should consider the item-varianceweighting in the traditional IBCF.
TABLE 5.1: Example of user-item rating matrix RM

U1
U2
U3

Score
3
4
5

Item 1
Rated time
2016-7
2016-7
2016-7

Score
3
4
5

Item 2
Rated time
2016-7
2016-7
2016-7

Score
3
4
5

Item 3
Rated time
2015-7
2015-7
2015-7

5.4. Time-related correlation degree and covering degree for the traditional IBCF 59

5.4 Time-related correlation degree and covering degree forthe traditional IBCF
5.4.1 Motivation of proposed approach
Our novel approach increases the weights of items that make a more significant contribution to the process of similarity computation and rating prediction, to extract
more precise and satisfactory recommendations from the RS.
In the IBCF approach, computation of the item-item similarity and rating prediction are separate procedures (Goldberg et al., 1992). In current versions of IBCF,
however, the two procedures give the same weights to all items. In reality, a person’s
preferences may change over time, and a different rating score may be given by thesame user to the same item. When computing item-item similarity, therefore, a time
factor should be included. In addition, items with lower similarity to the target item
may nevertheless make a significant contribution to the prediction, so weightings
should be given to the items used to make rating prediction.

5.4.2 Time-related correlation degree and covering degree
People are most likely to be interested in an item that they have evaluated recently.
So, for the same user, the score given to an item that was rated at approximately
the same time as the target item will make greater contribution to the similarity
computation. A person’s memory can be represented as a linear curve, first changing
fast, then more slowly. To express the degree of correlation between two items overtime, Ding and Li (Ding and Li, 2005) proposed a time function as follows:
f (t) = e−λ∗t ,(5.1)

where, λ = T10 is the decay rate, if T0 = 30 days, the time weight reduces by month.
Here, based on the time function above, we proposed the following time-related
correlation degree:f (Iu (x, y)) = e−λ∗|tu,x −tu,y | .

(5.2)

Here, f (Iu (x, y)) is a gradually decreasing function tracking the degree of correlation between item x and item y for a target user u, and tu,x is the time at which itemx was rated by user u. From the function f (Iu (x, y)), as the times at which two items
were rated by the target user become closer and the value of |tu,x − tu,y | becomes
smaller, the degree of correlation between the two items increases. The time-related
correlation degree function can therefore effectively estimate the relevance of an item
and increase the weight of items that were rated closer in time to the target item.
It is well known that a person’s current interests are strongly correlated withprevious preferences. If an item has characteristics that are close to the target user’s
known previous interests, this item should be given a greater weight when making
predictions. Here, we propose the covering degree function as follows:Let ⟨T, C⟩ be a covering approximation space. For a set X ⊆ T and K ∈ C, we
define the covering degree as{card(K∩X)
if K ̸= ∅,card(K)(5.3)
CD(K, X) =0,
if otherwise.
It is clear that CD(K, X) ∈ [0, 1], and the value CD(K, X) can be interpreted
as the degree to which the element K of covering C is included in the set X. In

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Chapter 5. Item-variance weighting for IBCF by using time-related correlation
degree and covering degree

RSs, an item’s neighborhoods are most relevant to the item itself and could express
the common characteristics of the item. If the target user’s preference set is treatedas X, then the item’s neighborhood set can be considered to be K. If an item has ahigher covering degree, CD(K, X) has a greater value, and the item should be givena greater weight when predicting the rating score for the target item.

5.4.3 Procedures of proposed approach
In our proposed approach, time-related correlation degree and covering degree areapplied to compute item-item similarity and rating prediction respectively. Furthermore, θ is set as the threshold for rating score, and items with ru,x ≥ θ are defined
as items relevant to user u. An target user tu does not need to input any special
information, then the new approach could output recommended items Rec.
Step 1: Item-item similarity computation with time weight. According to theuser-item rating matrix RM , we insert the time-related correlation degree into Pearson correlation coefficient to compute the item-item similarity. Here, we have∑sim(x, y) = √∑u∈Ux ∩Uy (ru,x −rix )∗(ru,y −riy )∗f (Iu (x,y))
√∑
¯ 2
¯ 2
u∈Ux ∩Uy (ru,x −rix )u∈Ux ∩Uy (ru,y −riy )

¯

¯

.

(5.4)

c , we selectStep 2: Neighborhood selection. From the similarity list of item i ∈ Itu
top k items as item i’s neighborhood Ni (k). In items domain I, for each similar itemj ∈ Ni (k) of the item i, we further select top q items from the similarity list of item j,which comprise the item set Cj . Let C∗ = I − ∪Cj . C = {C1 , C2 , ..., Ck , C∗} is thena covering in domain I.
Step 3: Rating prediction. In the domain I, relevant items of each target user tu
will comprise the relevant set Rtu , where

Rtu = {p ∈ I|rtu,p ≥ θ}.

(5.5)

Then based on the covering degree, in the neighborhood Ni (k) of the item i, for each
item j ∈ Ni (k), we compute the covering degree between each item set Cj and Rtu ,cand apply it to weighted sum approach to predict the rating score of the item i ∈ Itu
from the target user tu, here∑j∈N (k)∩Itu sim(i, j) ∗ rtu,j ∗ CD(Cj , Rtu )ptu,i = ∑ i.
(5.6)
j∈Ni (k)∩Itu |sim(i, j) ∗ CD(Cj , Rtu )|Step 4: Item recommendations. When all predictions are completed, the top N
items in the prediction list are selected as the recommended items.

5.5 Experiments and evaluations
In this section, we describe the evaluation dataset and metrics, examine the performance of the new approach, and compare it with the traditional IBCF approach.

5.5.1 Experimental setup and evaluation metrics
In our experiments we used the MovieLens 100K dataset (Herlocker et al., 1999),
which is often used to evaluate RSs. In our study, movies rated above 3 were treatedas relevant to that user. For every user, 20% of the rated items were treated as test
items and the remaining 80% as training items. Our experiment predicted a rating5.5. Experiments and evaluations

61

Algorithm 5.1 Proposed approach
Input: User-Item rating matrix RM with rating time and an target user tu.
Output: Recommended items set of size N for the target user tu.
SLti : Similarity list of item i with time weight.
Ni (k) : Neighborhood of the item i.
Rtu : Relevant items of the target user tu.
k : Number of items in the neighborhood Ni (k) of the item i.
N : Number of items recommended to the target user tu.
c : Items which have not yet rated by the target user tu.
Itu
cdptu,i : Rating prediction of item i for the target user tu with covering degree.
1: Apply the time weight to similarity measure, and compute similarity between
each item in I;
c do
2: for each item i ∈ Itu
3:
Find the k most similar items in SLti of item i to comprise neighborhood Ni (k);4:for each item j ∈ Ni (k) do
5:
The neighborhood of item j comprise the item set Cj . Use the covering
degree function to compute CD(Cj , Rtu ).
6:
end for7:
Apply the covering degree to the prediction function. Predict rating score pcd
tu,ic by the ratings of N (k) from the target user tu;
for item i ∈ Itui
8: end for
9: Recommend to the target user tu the top N items having the highest predicted
rating scores.

score for each test item based on the training items. As the MovieLens dataset wascollected over a seven-month period, we were able to use the month of rating to test
our time-based correlation degree function.
The mean absolute error (MAE), root mean square error (RMSE), precision, recall,and F1 were used as evaluation metrics, all of which are widely used for evaluating
RSs. MAE and RMSE, which compare the numerical prediction values against theoriginal user ratings, are the measures most commonly used for evaluating the accuracy of a recommender method. We have given the information of MAE and RMSE
in Subsection 3.5.1, here we present the definition of precision, recall and F1. Let
Rectu as the set of N recommendations to the target user tu. Precision is the proportion of recommended items that the target user actually liked in recommendations.
This measure is as high as possible for good performance.
P recision =1 ∑ #{i ∈ Rectu |rtu,i ≥ θ}
.
|U |
N

(5.7)

tu∈U

Recall indicates the proportion of relevant recommended items from the number of
relevant items. This measure should be as high as possible for good performance.
Hence, the recall is computed as follows:
Recall =1 ∑ #{i ∈ Rectu |rtu,i ≥ θ}
.
|U |
Rtu

(5.8)

tu∈U

F1 is a combination of precision and recall.
F1 =2 ∗ P recision ∗ Recall.
P recision + Recall

(5.9)

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Chapter 5. Item-variance weighting for IBCF by using time-related correlation
degree and covering degree

Precision, recall, and F1 indicate how well the RS discovers items that are desirable to the target user. In calculating the precision, recall, and F1 values, the numberof recommendations was set to 2, 4, 6, 8, 10, and 12.

5.5.2 Experimental results and comparisons
We named the IBCF approach incorporating time-related correlation degree and covering degree TCIBCF. To test the performance of our new approach, we compared
its results with those from the standard IBCF. Additional, we also made comparison with popular approach TWIBCF presented by Ding (Ding and Li, 2005). In theexperimental testing of the IBCF approach, the Pearson correlation coefficient wasused as the similarity measure, and the weighted sum was used to predict the ratingscore. To evaluate the TCIBCF approach, we then added the time-based correlation
degree and covering degree to the similarity computation and rating prediction.
Table 5.2 shows the results of the different metrics applied to TCIBCF, TWIBCF,
and IBCF. To set the neighborhood size, we selected different numbers from the similarity list as items similar to the target item. In the TCIBCF approach, the size of thesimilar items k was equal to the size of each similar item’s neighborhood q. It canbe observed from Table 5.2 that, using the TCIBCF approach, the MAE and RMSE
values became smaller as the neighborhood size increased. As lower values of MAE
and RMSE indicate better accuracy, the accuracy of TCIBCF improved as the neighborhood size increased. In contrast, using the IBCF approach the values of MAE and
RMSE did not decrease linearly as the neighborhood size increased. The best MAE
and RMSE values of 1.192 and 1.390 were recorded when the size of neighborhood
was 20, and the worst values of 1.217 and 1.424, when the size of neighborhood was
10 and 40 respectively. For TWIBCF approach, although the values of MAE and
RMSE were smaller than IBCF approach, they were also bigger than TCIBCF approach. Beside that, same as IBCF approach, values of MAE and RMSE for TWIBCF
approach also decreased nonlinearly as the neighborhood size increased. The best
MAE and RMSE values of 1.181 and 1.339 were recorded when the size of neighborhood was 20, and the worst values of 1.206 and 1.396, when the size of neighborhood
was 10 and 50 respectively. Overall, the proposed TCIBCF approach outperformed
the traditional IBCF and TWIBCF approaches in terms of MAE and RMSE metrics.
Figures 5.1, 5.2, and 5.3 show the precision, recall, and F1 measures for T CIBCFk=50 ,T W IBCFk=50 , and IBCFk=50 . As can be seen, in the precision measure, the TCIBCF
approach was stable, and the TWIBCF and IBCF had a small fluctuation as thenumber of recommendations increased; however the proposed TCIBCF approach
had higher precision values than TWIBCF and IBCF approaches. For recall and F1
measures, all the TCIBCF, TWIBCF, and IBCF approaches increased significantly as
the number of recommendations increased. Furthermore, at first, recall and F1 of
TCIBCF were lower than TWIBCF and IBCF when the number of recommendations
was 2, however as the number of recommendations increased, TCIBCF had faster
improvements, and had much better values than TWIBCF and IBCF approaches.
Overall, the proposed TCIBCF approach could make better recommendations than
the traditional IBCF and TWIBCF approaches in terms of precision, recall, and F1
metrics.

5.5.3 Analysis and discussion
The most significant innovation in our approach is the ability to weight items thatmake a greater contribution to the similarity computation and rating prediction. As

5.5. Experiments and evaluations

63

0.60

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IBCF
TWIBCF
TCIBCF

0

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4
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Number of Recommendations

12

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F IGURE 5.1: Precision of the TCIBCF, TWIBCF, and IBCF against the
number of recommendations

the key items then play a more significant role in the RSs, the recommendations
generated are closer to the real tastes of the target user.
Experimental and comparative results suggest that our new TCIBCF approach
can achieve better accuracy than the IBCF and TWIBCF approaches, as the predicted scores which the TCIBCF generated more closely matched the original scores.
The TCIBCF also achieved better precision, recall, and F1 values than the IBCF andTWIBCF approaches. This suggests that a target user will agree with more recommendations made by the proposed TCIBCF than with those produced by IBCF andTWIBCF approaches.
In our approach, items are given different weights in the item-item similarity
computation. For example, take two items x and y that have the same rating scoresfrom the target user tu, so that ttu,x = ttu,y . The time at which target user tu rated
item i is ttu,i . This is closer than the rating time of item y, so that |ttu,i − ttu,x | <|ttu,i − ttu,y |. In a traditional IBCF, items x and y will be given the same weight when
computing item-item similarity, because they received the same rating score fromthe target user. However, the preferences of target user tu may have changed, sothat the rating of item x will have a greater influence than that of item y. In our
approach, f (Itu (i, x)) > f (Itu (i, y)), so item x will carry a higher weight than item ywhen computing item-item similarity. After the similarity has been computed, items
similar to the target item were selected to predict the rating score. Here, we used thecovering degree function to compute the weight of each similar item. Item set Cj

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Chapter 5. Item-variance weighting for IBCF by using time-related correlation
degree and covering degree

0.00

0.10

Recall0.20 0.30

0.40

0.50

Recall Measure

IBCF
TWIBCF
TCIBCF

0

2

4
6
8
10
Number of Recommendations

12

14

F IGURE 5.2: Recall for the TCIBCF, TWIBCF, and IBCF against the
number of recommendations

captures the characteristics of similar item j. If Cj has a greater degree of inclusionin the target user’s previous preferences Rtu , so that the value of CD(Cj , Rtu ) is high,this suggests that the characteristics of item j are nearer to the target user’s previous
preferences, and that this item is more likely to be preferred by the target user. Itemj will therefore be given a greater weight when making the rating prediction.

5.6 Summary
IBCF is an important technology which is widely used in RSs. This approach builds
an item-item similarity matrix as the basis for rating predictions. The IBCF approachhas good scalability and can be used with large-scale data information system. However, in IBCF all items are treated as having the same weight. In reality, a customer’s
preferences may change over time, so that a time factor should be used when computing item-item similarity. In addition, items that appear similar to the target item
make different contributions to the rating predictions, so the relative weights of similar items also need to be taken into account.
In this chapter, we introduced a time-related correlation degree function, allowing a time factor to be applied to item-item similarity. For each target user, items
that were rated nearer the time of the target item rating will have greater weight
when computing item-item similarity. We further added the covering degree function to the rating prediction procedure. Items with a higher covering degree with

5.6. Summary

65

0.10

0.20

0.30

F1

0.40

0.50

0.60

F1 Measure

IBCF
TWIBCF
TCIBCF

0

2

4
6
8
10
Number of Recommendations

12

14

F IGURE 5.3: F1 for the TCIBCF, TWIBCF, and IBCF versus the numberof recommendations

the target user’s relevant set will have greater power to predict the rating score of
the target item. The results of our experiments demonstrated that our novel TCIBCF
could achieve better accuracy and higher precision than existing work, making it aneffective approach to RS.

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Chapter 5. Item-variance weighting for IBCF by using time-related correlation
degree and covering degree

TABLE 5.2: Results of MAE and RMSE metrics

Approaches
T CIBCFk=10
T W IBCFk=10
IBCFk=10
T CIBCFk=20
T W IBCFk=20
IBCFk=20
T CIBCFk=30
T W IBCFk=30
IBCFk=30
T CIBCFk=40
T W IBCFk=40
IBCFk=40
T CIBCFk=50
T W IBCFk=50
IBCFk=50

MAE
1.032
1.206
1.217
1.072
1.181
1.192
1.132
1.186
1.205
1.144
1.198
1.216
1.164
1.189
1.206

RMSE
1.200
1.358
1.397
1.259
1.339
1.390
1.345
1.387
1.403
1.361
1.377
1.424
1.382
1.396
1.414

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Chapter 6

Conclusions and future work
6.1 Thesis summary
With the rapid development of technology, the data and information has been increasing at a dramatical speed. Therefore, more customers are facing the problem
of discovering the demanded contents from overwhelmingly massive data. As theresult, this problem becomes a popular research topic and attracts attention fromlots of scientists. RSs can properly deal with the information overload problem,
which are widely welcome by users and thus adopted by great amount of websites
and corporations. Many approaches are proposed for RSs by far, CF is the mostsignificant and successful approach among them. Nevertheless, some problems inexisting CF approach could prevent the further development of CF in RSs. For example, UBCF cannot provide recommendations for an active user with satisfactory
accuracy and diversity simultaneously. Personalized recommendations cannot beprovided by UBCF for a new user which often has insufficient information. In addition, items that make a more significant contribution cannot have high weighting in
IBCF.
In this dissertation, to address and solve problems mentioned above, we utilize covering-based rough sets as our research method, and improve the traditional
UBCF and IBCF respectively. The main work of this dissertation is summarized asfollows:
First, a CBCF approach is proposed to improve the traditional UBCF for active
users’ personalized recommendations. For traditional UBCF, in candidate neighbors of an active user, because there exists some redundant users who have higher
similarity but can make predictions only for a few types of items, the traditional
UBCF usually can not provide recommendations with satisfactory accuracy and diversity at the same time for an active user. Combining with the characteristics ofredundant users in UBCF and redundant elements in covering-based rough sets,
CBCF inserts a neighbor selection procedure into the traditional UBCF that could remove redundant candidate neighbors by covering reduction algorithm. To remove
as many redundant users as possible, according to the sufficient information from anactive user, we first extracted relevant attributes of the active user, then constructed
decision class by all items that fit the active user’s relevant attributes, and reduced
the domain from all items to decision class. Experimental results suggest that CBCF
outperforms than the traditional UBCF and can provide recommendations with satisfactory accuracy and diversity simultaneously for an active user.
Second, CBCF approach is improved for new users’ personalized recommendations. For the previous CBCF, new users’ ratings are usually very few, and it is unreliable to extract relevant attributes according to a new user’s rating information.
Moreover, in the previous CBCF, the item attribute matrix had to be inputted as theindispensable condition, even though some datasets do not have this information.

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Chapter 6. Conclusions and future work

Therefore, we improve previous CBCF approach, and reconstruct the decision classfor the new user as the set of niche items in the dataset used for recommendation.
In this way, redundant candidate neighbors for a new user are able to be removed
as many as possible, and the decision class is easily constructed from the user-item
matrix without needing special additional information. Experimental results suggest that the improved CBCF significantly outperforms those of existing work andcan provide personalized recommendations with satisfactory accuracy and diversity
simultaneously for a new user.
Third, a TCIBCF approach is proposed to improve the traditional IBCF for itemvariance weighting. For the traditional IBCF, all items are accorded the same weight
when computing the similarity and making predictions. However, different items
have different contributions to the process of similarity computation and rating prediction, some items that make a more significant contributions should have higher
weighting. Therefore, we present time-related correlation degree and covering degree, and apply them to the traditional IBCF to propose TCIBCF. TCIBCF is able toincrease the weighting of items that make a greater contribution to the similarity
computation and rating prediction. Experimental results suggest that TCIBCF canproduce recommendation results superior to those of existing work.

6.2 Future research directions
While the current dissertation can address some problems of CF in RSs, some aspects
remain to be investigated in future studies.
First, because the proposed CBCF belongs to CF approaches, so although the
CBCF approach is proposed to focus on improving the traditional UBCF, the principles of CBCF approach can also be incorporated into IBCF; however, how to define
the redundant items requires further consideration. On the other hand, the CBCF
approach aims to improve personalized recommendations for both new and active
users, with the result that we can summarize it to propose a CBCF framework for
RSs.
Second, the proposed TCIBCF approach can be further extended to address the
new item cold-start issue, which is a very difficult problem faced in RSs, because a
new item only has been rated by few users, so we cannot obtain sufficient information from the new item.

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Appendix A

Papers published during thedoctoral period[1] Zhipeng Zhang, Yasuo Kudo, Tetsuya Murai (2016). "Neighbor selection for userbased collaborative filtering using covering-based rough sets". In: Annals of Operations Research. doi:10.1007/s10479-016-2367-1.
[2] Zhipeng Zhang, Yasuo Kudo, Tetsuya Murai (2016). "Improvement of item-based
collaborative filtering by adding time factor and covering degree". In: Proc. of 8th
Int. Symposium on Soft Computing and Intelligent Systems and 17th Int. Symposium on Advanced Intelligent Systems (SCIS&ISIS2016), SOFT, pp. 543-547.
[3] Zhipeng Zhang, Yasuo Kudo, Tetsuya Murai (2015). "Modification of the coveringbased collaborative filtering model to alleviate the new user cold-start problem". In:
Proc. of 16th Int. Symposium on Advanced Intelligent Systems (ISIS 2015), KIIS, pp.
1238-1249.
[4] Zhipeng Zhang, Yasuo Kudo, Tetsuya Murai (2015). "Applying covering-based
Rough set theory to user-based collaborative filtering to enhance the quality of recommendations". In: Proc. of 4th Int. Symposium on Integrated Uncertainty inKnowledge Modeling and Decision Making (IUKM 2015), LNAI 9376, pp. 279-289.