WSEAS TRANSACTIONS on COMPUTERS

Chhavi Rana, Sanjay Kumar Jain

Building a Book Recommender system using time based content
filtering
CHHAVI RANA
Department of Computer Science Engineering, University Institute of Engineering and Technology,
MD University, Rohtak, Haryana, 124001, INDIA.
chhavi1jan@yahoo.com
SANJAY KUMAR JAIN
Department of Computer Engineering, National Institute of Technology, Kurukshetra,
Haryana,136119, INDIA.
skj_nith@yahoo.com
Abstract: - Recommender System are new generation internet tool that help user in navigating throughinformation on the internet and receive information related to their preferences. Although most of the time
recommender systems are applied in the area of online shopping and entertainment domains like movie andmusic, yet their applicability is being researched upon in other area as well. This paper presents an overview of
the Recommender Systems which are currently working in the domain of online book shopping. This paper
also proposes a new book recommender system that combines user choices with not only similar users but otherusers as well to give diverse recommendation that change over time. The overall architecture of the proposedsystem is presented and its implementation with a prototype design is described. Lastly, the paper presents
empirical evaluation of the system based on a survey reflecting the impact of such diverse recommendations on
the user choices.
Key-Words: - Recommender system; Collaborative filtering; Content filtering; Data mining; Time; Book
user navigation history and his similarity with otherusers. Recommender System gives a list ofrecommendations to the user which is an attempt ofpredicting user’s preferences. A website using a
recommendation system can more effectively
provide a user with useful and relevant suggestionthat could fulfil his current information requirement.
As such, these websites have an edge over others in
gaining customer loyalty as well as long term
partnership.
The term recommendation system was first
introduced by Resnick and Varion [13] to generalize
the concept of collaborative filtering [7]. They
implemented the first recommender system using
collaborative filtering technique. Since then the termis being used by different researchers and is
explained in various ways. The most common
technique used for building recommendation system
is collaborative filtering, so much so that both theseterms are used interchangeably by many
researchers. There are few other techniques also
which are used for making recommendations suchas content based filtering, demographic andknowledge based technique but they are not so
widely applied. Collaborative filtering is more
successful applied technique, prominent example of

1 Introduction
Internet and World Wide Web is changing the way
people live and communicate with each other.
Together with this, there is lot of information
bombardment on the user who wants to access some
information on the internet. In the midst of thiscomplex environment of web, Recommender
System (RS) serves as an agent that helps user ingetting the relevant information. With the growth of
economy and advent of new technology, lots ofpeople are using internet as a source of information
for making comparative analysis of products thatthey would like to buy online. As such lot of
websites are also offering books as one of the
product and some sites are dedicated websites foronline book shopping People are increasingly using
internet to choose the best deal to spend their moneywith a widely available list of authors and genres.
In this competitive market, lots of vendors
are employing different strategies to attract
customers. The customers are bombarded withinformation on the internet, finding relevant among
which is a dotting task. Recommendation systems is
one such technology that help user by providingthem with relevant information based on theirprofile. A user profile is generated on the basis of

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which is amazon.com. In this technique, the user isgiven recommendation on the basis of similar user
profiles which are calculated through various
measures. Mostly Pearson correlation coefficient,
Cosine similarity measure and Euclidian distanceare taken as widely applied similarity measures.
A number of recommender systems areapplied in various domains on the internet and eachone of them tries to accurately predict user
preference. Also, various researchers studied
different facts of recommender system likesemantics, scalability etc. Another dimension which
is being highlighted lately is the temporal dimensionthat incorporates changes in the recommendation
process over a period of time [8]. It is being
observed that with time user preference changes aswell as the system content also gets updated [3].
Thus, the recommendation models that predict user
preference needs to be updated by refreshing theuser profile at regular intervals to present relevant
recommendation for users. The effect of temporal
dynamics in collaborative filtering [8, 9] is being
studied lately, but its effect on content based
filtering [2] is yet to be taken up.
In this paper, we have developed a book
recommendation system that is based on content
based recommendation technique and takes into
account the choices of not only similar user but allusers to predict new recommendations for the user.
This approach combines user choices with the mostwidely visited links over a period of time and thus
gives a set of diverse as well as most widely usedpreferences. The paper also analyzes the effect of
temporal factor on content based recommendation.
Though a number of books Recommender Systemalready exist, but none have so far implemented thetime factor on content based recommendation. We
implemented a system which will use a counter foreach item that gets updated with time in relation toother items and combined it with content based
recommendations. As such, the paper presents acompletely new outlook on the effect of time factor
on the recommendation process in a content based
environment. Though collaborative filtering is
widely used methodology in most of therecommendation systems, content based technique
can have leverage where there are somecharacteristic values that represent item content andthis is the case with books.
Our proposed system ensures thatrecommended items remains relevant to thechanging user preferences. In Section 2 literature
survey of existing book recommendation system ispresented. Section 3 introduces the overall design
and implementation of the content based RS.

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Section 4 describes the evaluation of the proposedsystem with respect to diversity and temporality.
Finally, section 5 provides concluding remarks.

2 Current Recommender System
Now a days Recommendation systems are applied
to a wide range of domains, though initially theirfocus primarily was on the entertainment domain
and online shopping sites. Currently there use isbeing extended to social network sites, e-leaning
and travel domain. The focus area of this paper isrelated to book and as such we present here an
overview of the Book Recommender Systems. They
help users in managing their reading list by learning
their preference. There are two category of such
system, one which gives a list of recommendation
based on user profile in a library automation systemand others which tells user specifically what should
he read next according to the current requirements.
There are sites like whichbook.net, what should I
read next, lazy library, library think etc., each ofwhich uses a particular strategy to fulfil user current
requirement. A number of such recommendersystems are described hereby.
Whichbook.net allow users to select aparticular category, plot and settings for giving
consequent recommendations. Thus, the focus of thesystem is on users current information need. Also
this system allows user to select a particular mood
like happy, funny, unusual and unpredictable and at
the same time allows them to change their search
criteria between moods and plot setting. The
diversity quotient of such recommendation isnarrowed within the unpredictable option andtherefore user could find the system repeating
similar books with passage of time.
WhatshouldIreadnext.com
uses
collaborative filtering technique and give
recommendations to the user by using an author or
title of a book and matching it with others reading
list. Such technique generally makes an assumptionthat similar users have similar preferences [4]. Thus,
the users having some books common in theirreading list can be given recommendations of others
books which are not in their reading list. Suchpreferences could get stale after a period of time as
similar books will be repeatedly given as
recommendations.
Lazylibrary.com search books with topic
wise listing and have a limit on size of book. Thus
the system uses content based filtering approach thatchooses books with similar content [1]. The novelty
about this system is the restriction on the number of

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page of the book which can also be a stumbling
block in giving relevant recommendations.
Librarything.com is a completely different
system which is best known for cataloguing bookinto category for each user. It uses a hybrid
technique that combines recommendation that are
based on library of congress categories and what
other similar uses have in their reading lists. It also
includes links to a number of books stores as well
[11,14, 5]. The major drawback is that it assumes
that users have already looked at all the books bysame author and thus select something completely
different which could be irrelevant for the user.
Abebook Corporation which own the world
largest online book store have also developed andlaunched a new online book recommender systemcalled Bookhints.com based upon title owned, readand enjoyed by members of Librarything.com, theonline cataloguing site. It uses the database oflibrary thing and generates recommendations based
upon titles found on the catalogues of similar users
who also own the book originally sought. “It is thefirst time that unique information added by an
online community has been directly applied toretailing”, said Boris Watz, COO of Abebook.
Another book recommended system
Booklamp.com uses a novel technique to predict
user preference. It matches readers to a book by
analyzing the writing style, perspective anddescription of the whole book together and thus
allows users to find books with similar tones, actionlevel and dialog level. It removes outside influencessuch as advertising, author marketing or social
networking. As the system has grown, it has also
incorporated human feedback and mixed it withcomputer analysis. It allows user to match bookswith far greater details than any other such system.
Goodsreads.com, a social network for booklovers was started in 2006. Initially it does not havea recommendation system embedded in it, but lately
it acquired discoverread.com which has a strong
recommendation algorithm. Discoverread.com
analyzes book trends and give recommend on thebases of user rating by matching it with friends andsimilar users [13]. The major drawback with thesystem is that as the system becomes popular, more
and more irrelevant ratings are added which could
harm the accuracy of recommendations.
Bookexplorer.com
isanotherrecommendation engine in which the books arearranged category wise for user and there is also asearch option for users. Once user selects a bookfrom a category or from the search option, lists ofrecommended books are also presented to the user.
The recommendation system is fairly simple as it

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implements content based filtering within thesimilarcategory
of
books
to
present
recommendation. The drawback of the system lies
in its ability to handle diverse recommendation.

3 Proposed System
The book recommender system implemented till
now utilizes either similar user interest or item
characteristic for matching it with user profile forgiving future recommendation. Our recommendersystem adds another important dimension to thiswhich is the temporal dimension. This dimension
takes into account the number of time an item isliked by the user over a period of time. A counter isstored for each item which is updated whenever auser checks that items in its favourite links. The
greater the counter for any particular book, the more
are the chances of it being recommended. Moreover
instead of using counter rating of similar user, ouralgorithm uses counter ratings of all users that
improves diversity of the recommendation process.
Over a period of time, the counter for different items
will change in relation to other items which could
either become more favourite or less favourite incomparison to each other. Thus, the overall system
reflects an updated favourite list with respect to aparticular category at any given point of time. User
preferences are incorporated through content based
filtering where only a particular category of bookwill be recommended. The user will be given asearch option as well a category listing to choose abook and once it chooses a category or book title,
recommendation will be enlisted. The timely
updation of recommendation is a very important
aspect as user need as well as the system content
keeps changing with time [3]. Thus, there should bea mechanism to precisely incorporate these changes
into the recommendation process. This will giveuser more relevant, diverse as well as updated
information which establishes the effectiveness ofRecommender System.

3.1 System Overview
The proposed approach is very simple and it
effectively improve the recommendation process by
incorporating diverse as well as updated
recommendations by maintaining a simple counterfor each item. The recommendation process isdivided into four categories:(a) Gathering of useful web data(b) Pre-processing of the collected data(c) Interpretation of the web data(d) Decision making or Final Recommendation.

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certain domain like books, e-learning and Academic
research content based filtering can be more usefulthan collaborative filtering [10]. This is due to thefact that such domains offer lots of features that canbe easily used for matching users and items. As such
we have focused on content based filtering andadded a feature that could combine updated as well
as diverse information into the system with passageof time. There is no need to communicate withother users in such systems [2]. The major strengthof content filtering is that even if the system has
fewer ratings or none at all which usually is in caseof most of the new users, recommendations can be
made. The only requirement is to have some
information about each item which is easily
available in case of book recommender system.

When user sends request for a particular book or
author in the form of a query to the Search Engine,
the Search Engine return the response by giving
URL’s of all the matching links depending upon thealgorithm used by the Search Engine. The proposedsystem uses content based filtering algorithm over acategories of books to search similar books.
Moreover, books which are listed as favourites by
maximum number of users are given as
recommendations. When user enter query for its
particular search request, the Query is firstprocessed to filter out irrelevant data and thenanalyzed to discover interesting navigation patterns
and relation between books category and countervalue of items in that category. A response
repository is formed which serves as a knowledge
base. After building Knowledge Base, system cangive recommendations based on the navigation andmost visited links by particular user in the featured
domain. The Personalized data is then given as a
input to the ranking algorithm for personalizing theresult according to the most visited links or
favourite links of the particular user by evaluating
web mining results. Thus, the System helps the user
to retrieve his/her interested domain by clicking on
the list of his/her favourite links with moreconvenience. The current recommendation is basedon the recent most last selected item by the user.
The content are filtered according to this category
and the items with highest number of favourite
checks at this point of time are given as
recommendations. Fig 1 shows the overallarchitecture of the proposed book recommendersystem.

3.2 Process flow of the System
The personalized book recommender system
consists of the following workflow -:1) Firstly user casually uses the system without
registration and collects raw information i.e.
information which is available to the every
visitor’s of the website. After analyzing thisinformation, user login to the system andgenerates its profile which contain user
personal details and most visited link
recommended by the system based on the
URL ranking algorithm (collectively mostvisited favourite links of all users).
2) After the registration is completed and a setof general recommendation with mostvisited links provided, the system proceeds
on to next level. Based on the user
navigation pattern, system analyzes user
behaviour and predict its interest by
performing content filtering on the basis of
this analysis. As a result, system provide
favourite link to the user based on its profile
via
personalized
web
page
and
simultaneously update already exiting links
on the basis of periodical temporal update.
3) In the last step, the system again perform
analysis on the previous result and deliver
top n recommendations of that category ofbooks in which user is most interested in byfiltering the contents available on the system
by performing content filtering.
The result of the overall process workflow is thatsystem deliver web page to user which contain its
personal detail, most visited link of the site, user’s
favourite link and contents recommended by thesystem of his/her interested domain in single
customized format.

Fig. 1. Architecture of Book Recommender System
The proposed system tries to give diverse
recommendations to the uses which are also relevant
to his current information needs. In the area of

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Chhavi Rana, Sanjay Kumar Jainof which five focuses on demographic data (genderand age) so that results could be categorized. The
other questions were specifically focusing on
recommendation relevancy to the user, accuracy
with temporal changes and diversity.

This figure below depicts overall process
workflow for book recommender system.

4.1 Implementation
The system was implemented using ASP.net as front
end and SQL server as backend on an Intel
Core2duo processor with 2GB ram and 300GB hard
disk.

Fig. 2.
Process
Recommender System

workflowfor

4.2 ParticipantsParticipants were college students who were alreadyusing some book recommendation system and arerequested to use the current system. They
volunteered to take the survey by clicking on thelink on the website. Around 150 respondents
participated in the survey among which 50 girls and100 boys were there. Each participant belongs to
age group 18-25 years.

Book

4.3 Results
The results were inferred by using descriptive
statistics to generate percentages and further
analysis of the data gathered from the survey
through qualitative methods to discover major
themes [6].

3.3 Personalized Recommendation
The process of recommendation can enhance theoverall user interface of a particular website. The
user interface can further be enhanced if therecommendations are given in a particular mannerthat helps user in further navigation. In case ofbooks, most of recommender system provides amechanism through which user is presented withfurther links to buy recommended books fromonline stores. Other recommender system gives anoption of borrowing such books from library which
could be limited to particular country or city, as iscase with librarything.com. The emphasis suggestthat calculating recommendation list is as importantas presenting them in certain manner. Finally somefeedback mechanism could also be incorporated at
the end of the recommendation list that will takesuggestion from users and thereby improve therecommendation process. This will act as anunobtrusive way of getting suggestion from theusers at the end of the whole process.

4.3.1 Diversity Impact
In the first experiment shown in Figure 3, it is theevaluation of the recommendation preferences withrespect to diversity. The users are asked to give ascore to both types of recommendations. The higher
score
indicatesthemore
usefultherecommendations are. It can be seen that most of therecommendation that comes under diversity weremore liked by the users. Participants were asked to
explain their preferences regarding diverse
recommendation and content based similar
recommendation, and from the data five participant
preference categories were developed: Diverse,
Similar, both, neither, and no preference. Of the 74
participants that responded to the survey, themajority of the members, 54% (40) preferreddiverse recommendations while only 22% (16)preferred similar recommendations. The other 24%(18) of the participants preferred neither, both or
had no preference.

4 Research Design
The performance analysis of this system is doneusing an online survey to explore the effect of
temporal updation on user satisfaction. The study
also analyses how user reacts to a set of diverse
recommendations. There was no identifying private
data which was sought. There were 10 questions out

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45
40
35
30
25
20
15
10
5
0

Chhavi Rana, Sanjay Kumar Jain

4 Conclusion
In this paper, we propose a book
recommender system that uses content based
approach for recommending item to a particularuses. In this approach we embedded a new
dimension called temporal dimension using acounter for each item which gets update withpassage of time and thereby improving the whole
recommendation process.
The major goal of the current system is toprovide diverse recommendation to the user even if
there are fewer ratings about an item for a particularuser. The process also get updated with passage oftime which makes different item less or more
relevant according to their counter value which getschanges with time in relation to other items. The
literature survey listed a number of recommendersystem but none takes into account this criteria forsuggesting diverse as well as updated
recommendation. After a certain amount of time, theaccuracy of the recommendation process gets stuck
and users keep on receiving similar items. Further
accuracy can only be increased by taking into
account temporal dimension which is what thispaper addresses. Result shows that proposedarchitecture for the book recommendation engine
providediverseandtemporally
updatedrecommendation to the users which are more usefuland relevant.
The current system applies this approach in
the domain of book recommender system which
could further be extended to other domains as well.
Further the system can be extended in otherdimension that includes incorporating contextual
information, taking multiple ratings and providing amore flexible recommendation that could range into
different domains as well. The performance of thesystem and its comparison with the other similarsystems is scheduled for future ongoing work.

Diversity
Preffered
Similiarity
Preffered
Others
1st
2nd
3rd
4th
Week Week Week Week

Fig. 3. Diversity Effect4.3.2 Temporal Impact
In the second experiment, preferences of users overa period of time were analyzed. The users wereasked to rate the recommendation after using thesystem for a period of time on a daily basis and thenafter a week they were asked to give score about thenature of recommendation. Participants were asked
specificquestion
about
whethertherecommendation were changing with time and were
those changes helpful or not.
Users were also asked to rate the quality ofrecommendation on a scale of 1 to 10. Figure 4
shows that only 20 (26%) participants did not notice
any changes in their recommendations while 54(74%) participants see a noticeable change in theirrecommendations daily, weekly or periodically.
Most of the participants 37 (70%) among the 54
who noticed the changes, find those changes usefuland give a score 8 and above to the newrecommendations which were changing with time.
The above result shows that the newly developed
RS adapt to the user changing requirement in a very
effective manner.

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60
50
VisbleChanges40
30

Non Visible

20
Useful

10
0
1st
Qtr

2nd
Qtr

3rd
Qtr

4th
Qtr

Fig. 4. Temporal Effect

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