DEIM Forum 2017 F2-2

An Active Communication Mechanism based on User Behavior Analysis
for a Wedding Community Site
Toshinori HAYASHI† , Yuanyuan WANG†† , Yukiko KAWAI††† , and Kazutoshi SUMIYA†† Kwansei Gakuin University,
2–1, Gakuen, Sanda-shi, Hyogo, 669–1546 Japan†† Yamaguchi University,
2–16–1, Tokiwadai, Ube-shi, Yamaguchi, 755–8611 Japan††† Kyoto Sangyou University,
Kamigamo Motoyama, Kita-ku Kyoto-shi, 603–8047 Japan
E-mail: †{anc93184,sumiya}@kwansei.ac.jp, ††y.wang@yamaguchi-u.ac.jp, †††kawai@cc.kyoto-su.ac.jp
Abstract This paper presents an active communication mechanism based on user behavior analysis on wedding
community sites. For this, we propose a novel mechanism for activation of users’ communication which provides
related comments and users by detecting knowledge and interests from archived comments on a wedding community
website to evoke conversation among users. This mechanism has three components, 1) extracting user login information and user characteristics of their posts to know what they need to communicate, 2) detecting and recommending
users who are likely to communicate with each other, 3) recommending comments which users may be interested in.
Through proposed activation mechanism, users on the wedding community site can communicate with each othereasily and eﬃciently. In this paper, we discuss our proposed user characteristic extraction and user recommendation
methods using actual user posts from a wedding community website.
Key words user behavior analysis, wedding community site, communication

1. Introduction
In recent years, several researches using data from Social
Networking Service (SNS) has been done, it is important tocollect much data from SNS community site, such as Facebook, LINE, and some Q&A sites. However, they focusedon data collection, cannot promote user communication oncommunity websites because of diﬀerences in values. In thispaper, we aim to promote user communication focusing on awedding community site by recommending appropriate users

Figure 1 User and comments recommendation for activation ofuser communication based on user behavior analysis

and their comments.
In this paper, we propose a novel active communication

The rest of the paper is organized as follows. Section II

mechanism which share the comments of users by consider-provides an overview of our system and reviews related work.

ing their knowledge and interests by analyzing their behavior

Section III explains how to recommend users and their com-on community websites. To achieve this, we first extract allments on a wedding community site. Section IV illustrates

posts of each user and extract their feature words by using tf-

the experimental results obtained by using a real dataset of

idf method. Next, we calculate the similarities among users

a wedding community site. Finally, Section V concludes theto detect appropriate users. Finally, we recommend theirpaper and outlines our future work.

comments by generating links to them in the posts (Fig. 1).
To use this mechanism, users can communicate with other

2. System Overview and Related Workusers who are recommended to them about wedding plan-

2. 1 An Active Communication Mechanism

ning, and it will promote communication among users on

This paper presents an active communication mechanism

the wedding community site.

based on user behavior analysis on wedding community sites.
This mechanism has 3 steps, 1) user login information anduser characteristic extraction, 2) user detection and recommendation, 3) comment recommendation (Fig. 1).
To use this mechanism, users are required to install a toolbar (a browser plug-in) on an existing wedding community
site in Japan. There are some users logged in the wedding
community site, when they plan to hold wedding. The website is created for observing users’ needs about marriage. On
Figure 2 3 axises of user behavior

this website, there are some threads for wedding planning in
diﬀerent marriage statuses, users can freely post the comments to each thread. There is only one was the way tostudies about recommendation by analyzing user behavior

communicate with other users by replying to other users’

on news sites [6], did not consider the relationships between

comments on the thread. To improve it, we propose theusers. In this paper, we first extract users’ posts to analyze

method to recommend both users and their comments byuser behavior, and detect users to recommend by extracting

analyzing user behavior and their profile information on thethe relationships between the users.

wedding community site. The goal is for our active commu-

Akihiro et al. [7] conducted an experiment for activate com-

nication mechanism to figure out other users whom the users

munication in e-lectures through a chat system. However, it

want to communicate with.

did not work very well because it was a burden for students

The wedding community site is not a “a Question & An-

to chat with others during the lectures. In this paper, we

swer site”, is a website where users can share their positive

propose a new active communication mechanism by recom-

opinions and experiences about wedding. The proposed sys-mending appropriate users for diﬀerent marriage statuses’

tem will recommend other users who have similar situation

users.

or values of marriage to evoke communication between users.
This system can be used on other community websites. Since

3. Active Communication Mechanism fora Wedding site

the proposed system is considered on a wedding community
site, it uses the static information which the users registeredby answering several questions about ideal wedding ceremony
when they first create their accounts.

3. 1 User Behavior Analysis on a Wedding Community Site
To evoke communication among users, our active commu-

Fig. 1 shows the overview of our proposed mechanism.

nication mechanism recommends users and their comments

After a user posted, it analyzes the user behavior, and thenby analyzing user behavior on a wedding community site.

it recommends other users by calculating the similarities be-

According to our previous work [3], we knew that users can

tween them.

help other users when they search for the same web pages.

2. 2 Related Work

Furthermore, in general, users communicate with each other

Issac et al. [1] mentioned that communication is importanteasily when they are in similar status or situation. Therefore,

to discuss things and to work with others as a group. Theyin order to recommend users, we analyze the user behavior

said that communication makes people willing to contributeby using 3 axises (see Fig. 2). These are “Static Profilefor society on their study. It is eﬀective for communication

Information”, “Marriage Status”, and “Active Profile Infor-

on websites. Ellison et al. [2] focused on the SNS communi-

mation”.

ties. According to these studies, communicating with others

3. 1. 1 User Login Information Extraction

on SNS makes more people feel happy.

We extract user login information by acquiring the user

In our previous work [3], users can communicate with eachregistration information on the wedding community site that

other when they search for web pages. In this work, we ex-users input the first time they register on the website. Since

tend our previous work, to recommend users and their com-

all users are required to register their information at first,

ments based on link generation for a wedding community

when they use the wedding community site, such as theirsite. Although several automatic link generation methods

ages, places where they live, and marriage status. We divide

for websites have been studied [4], [5], they have focused on

the user login information as users’ static profile information

web pages for knowledge support only, they do not consider

and marriage status.

the communication among users. To solve it, our proposed

3. 1. 2 User Characteristic Extraction

method recommends users to evoke communication. Other

We extract user characteristics by extracting all posts ofTable 2 Recommendation situation for each User Pattern

Table 1 Five user patterns for recommendation
Pattern User (Who) Marriage

Sta- Static Profile Activetus (to who)1

After mar- Before

Profile Purpose

Pattern Purpose

After mar- After

NeutralSimilarGive advices

1

Give advises

the commentsfrom you

NeutralSimilar

Share

2

Share

After Login○○ is on the same sta-

SimilarDiﬀerent

Reference3

Reference

Links are generated in You can refer to ○○NeutralSimilar

Share

4

Share

After Login

NeutralSimilarGet advices

5

Get advices

Links are generated in ○○ can be a good ad-

Links are generated in ○○ needs some advice

riage
3

tus as you

Before mar- Before
riage

4

the comments

Before mar- Before
riage

5

Before mar- Afterriageeach user. Next, we calculate term frequency and document
frequency based on the tf-idf method by the following formulae:○○ is on the same sta-

tus as you
the comments

viser for you

their experiences or advices. Based on 3 axises we describedin the previous subsection, we classify five useful patterns ofusers on the wedding community site (see Table 1).

ni,jtfi,j = ∑kidfi = log

How

Information

riage
2

When

Information

(1)

nk,j

We detect the most similar user of each user for patterns
1, 2, 4, 5, and we detect the most diﬀerent user of each user
for pattern 3. Based on the above, these detected users will

|D|
dfi

(2)

Here, ni,j denotes the term frequency of a word ti in thebe recommended.
3. 3 Comment Recommendation

document dj . In this work, dj denotes the document which

3. 3. 1 Comment Extraction

is integrated by all posts of one user. Therefore, the number

In previous subsection, we explained how to detect users

of documents as the number of users on the wedding com-and recommend them to stimulate communication on themunity site.

nk,j denotes the sum of term frequency of

wedding community site. To recommend users’ comments,

all words in the document dj . |D| denotes the number of all

we calculate the most related comments of the recommended

documents, which also means the number of users. dfi de-users who are derived by using Eq. (3)notes the number of the documents which include the word

3. 3. 2 Recommendation Interfaceti .

Our active communication mechanism recommends users∑k

Based on the above, we use these obtained tf-idf values
feature words of each user as users’ active profile informa-

or users’ comments in diﬀerent scenes refer to each user pattern in Table 2.
This mechanism has two ways to recommend users. thetion.
3. 2 User Detection and Recommendation

first one is recommend users in the comments by generating

3. 2. 1 User Detection

links to them. The second one is recommend users on the

We detect users based on the similarities of “Active Profile

top page after login.

Information” between users by using the cosine similarity as

In the first one, the interface of recommendation for patterns 1, 3, and 5, this mechanism generates links in the com-follows:

ments. To generate links in the comments after users posted,
→
→
Sim(−
x,−
y)= √

∑|V |
∑|V |

i=1

we attach the links of users’ information or their comments

xi · yi(xi )2 ·
i=1

√∑

|V |
(y )2
i=1 i

(3)

→
→
Here, −
x denotes the feature vector of user x, and −y denotes the feature vector of user y. |V | is the number ofdimensions of the feature vector.
“Marriage Status” is the absolute value, such as ”before
marriage” or ”after marriage”. Therefore, it will not change
against any users. However, “Static Profile Information” and“Active Profile Information” are the relative values. it willchange depending on each user.
3. 2. 2 User Recommendation
We recommend users by considering users who have similar situation to communicate with each other easily to shareto related words by extracting user characteristics (featurewords).
In the second one, the interface of recommendation forpatterns 2 and 4, this mechanism presents users on the top
page on the website after they log in. This mechanism also
recommends users whom they can share the experiences on
the top page. We assume that the users would like to see
more users on the top page than the links generated in thecomments.

4. Evaluation
In this section, we first extract the actual data from awedding community site to verify the user characteristic ex-Table 3 Top-15 feature words of users A, B, C and D

PP
PP Method
PP
User
PP
A

1)

2)

3)

of, A, ceremony, Wedding cere- sister, Wedding ceremony, Earth- Wedding ceremony, sister, Earthmony, To, sister, I will, heart, Fam- quake

disaster,

Fukushima, quake disaster, Bata, Atten-

ily, aftter, Because, To, did, Et al., Bata, Irreplace, Chaya, sister, At- dance, heart, Fukushima, Chaya,Thattendance, Column, heart, Family, Irreplace, Family, sister, Column,
Safety, Stop, name

B

11, Safety, Influenceof, did, Better, object, pull, A mar- Fish paste, Toyama, red snapper, Toyama, red snapper, Fish paste,riage, I will, he, now, A student, gift, Girlfriend, object, luck, A stu- object, gift, Girlfriend, luck, A stuGeneration,

Learning,Toyama, dent, Surprised, Age, pull, Moun- dent, Surprised, Age, Mountain,

Now, ChestnutCtain, form, Chestnut, happiness

form, happiness, Chestnut, woman

did, of, Better, Reach, Day, That, It seems intriguing, Eve, Limou- Eve, It seems intriguing, Limoufriend, friends, ceremony, Wedding sine, The eve, First meeting, Face sine, First meeting, friend, Face toceremony, while, A, Before, First, to face, A van, friend, The other face, The best, A van, Move, The
Good

day, Reach, Move, The previous previous day, Festival, The otherday, Festival, The bestD

day, Hawaii, Fellow, Reach

A, of, did, one, This, Now, Better, Reserved, snow board, Lend- Reserved, snow board, Alternat“”, To, about, Place, Et al., Yo, ing, No, Alternating current, ta- ing current, male, Hair style, taFiltration, Meeting

ble, , Hair style, Comment, Fir- ble, board, , BGM, rooftop, Firing, male, rooftop, development, ing, Girlfriend, In Tokyo, developrelease, Frank

traction method by extracting feature words of all posts ofeach user. Secondly, we detect similar users by comparing
the cosine similarity with the collaborative filtering.
4. 1 Experiment 1: Verification of User Characteristic Extraction

ment, Comment

after marriage.
In the future, the results suggest that we need to remove
the common words, since there still have some generally used
words by using the methods 2) and 3).
As discussed above, we could confirm that many feature

To evaluate our user characteristic extraction, we ex-words of users are extracted eﬀectively by using tf-idf meth-tracted feature words of all posts of each user. We comparedods 2) and 3) . To detect the user characteristics with feature

3 methods as follows:

words, more advanced methods are required.

（ 1 ） tf

4. 2 Experiment 2: Verification of User Detection（ 2 ） tf-idf (df = all of users)

In our active communication mechanism, the similarities（ 3 ） tf-idf (df = the users before or after marriage)

between users is the key point for recommending users. In

We extracted 7,728 terms from 588 posts of users.

previous section, we described that we classify the users by

Table 3 shows top-15 feature words of users A, B, C, andD by each method. Bold words denote the feature words are

the similarities of 3 axises to choose the most suitable users
to promote communication.

related to these users. We found that many feature words

To evaluate the similarities between users, we compared

are proper nouns on the methods 2) and 3), such as “Fishtwo calculation methods, the first is the proposed method as

paste” and “Limousine”. However, on the method 1), wethe content-based recommendation method by using cosine

found common words that all users often use, there are no

similarity with the active profile information, and the othereﬀective words as feature words. We considered that cal-

one is the item-based recommendation method by using theculating with idf is more eﬀective way to extract featurecollaborative filtering with the static profile information andwords. However, there are no diﬀerences between the meth-marriage status. As mentioned before, we calculated the co-ods 2) and 3). The idf values imply how the words generallysine similarity based on the user characteristics which con-used by many users. If the value of idf is high, it implies thesist of feature words of each user. Therefore, each user hasword is rare among the user. If it is low, it implies the word

the feature vectors by tf-idf values. In Experiment 1, theis common among them. Because of them, there are no dif-method 2) is the most useful method for extracting feature

ferences between the posts of users before marriage and thewords. Then, we calculated the cosine similarity based on theposts of users after marriage. We considered that diﬀerent

feature words by the method 2). The collaborative filteringdefinitions of document groups which are not only before andis also a method to calculate similarities between users. This

Table 5 The collaborative filtering among 30 users

Table 4 The cosine similarity among 588 users

value# user combinations

value# user combinations

0 〜 0.1

154,132-1.0 〜 -0.9

00.1〜 0.2

16,158-0.9 〜 -0.8

00.2 〜 0.3

2,022

-0.8 〜 -0.7

2

0.3 〜 0.4

209

-0.7 〜 -0.6

4

0.4 〜 0.5

46

-0.6 〜 -0.5

5

0.5 〜 0.6

7

-0.5 〜 -0.4

8

0.6 〜 0.7

4

-0.4 〜 -0.3

8

0.7 〜 1.00-0.3 〜 -0.2

15-0.2 〜 -0.1

23method calculated the similarities by using the user login in--0.1 〜 026

formation as items of each user. Therefore, it mostly used

0 〜 0.1

35

for recommending other items to the users by the following

0.1〜 0.2

39

formula:0.2 〜 0.3

40

0.3 〜 0.438

0.4 〜 0.5

41

0.5 〜 0.6

41∑(x − x̄)(y − ȳ)Sim(X, Y ) = √∑∑2
(x − x̄)(y − ȳ)2(4)Here, it calculates the similarity between users X and0.6 〜 0.7

44

Y . On a wedding community site, the users create own ac-

0.7 〜 0.8

31

counts with answering questions about their wedding plan-

0.8 〜 0.9

26

ning. For example, “Do you agree that the marriage with

0.9 〜 1.0

9

simple style?” They can choose one answer that is “Strongly
disagree”, “Disagree”, “Neither disagree nor agree”, “Agree”
or “Strongly agree” for each question. We replaced the num-planning.

bers 1 to 5 for those answers. Then, we calculated the simi-

Through these results, we compared two similarity calcu-

larities by using these numbers. x̄ and ȳ denote the average

lation methods. Here, we focused on the user E who has theof the answer they chosen, which means it must be as “3” inhigh cosine similarity with other users and she often post on

this case as they chosen the answers 1 to 5.

the wedding community site as a main user. We calculated

The users that we evaluated for our proposed user charac-

all combinations with the user E, therefore, it has 588 values

teristic extraction are shown in Table 3. On this evaluation,of the cosine similarity and 588 values of the collaborative

we calculated totally 172,578 combinations of 588 users, andfiltering.

the value of the cosine similarity should be between 0 to 1.

Fig. 3 shows the distribution of the cosine similarity andTable 4 shows the distribution of the results of the cosine

the collaborative filtering for 10 users, E, H, I, J, K, L, M,

similarity. The average value of all combinations is 0.045.

N, O, and P. Each dot means one user and it has two values:

We found that many results of user combinations are be-

the cosine similarity with each user, and the collaborative

low 0.1. Because most users talk about diﬀerent things forfiltering with each user. The vertical axis implies the val-

their wedding planning. In addition, some user combinations

ues of the cosine similarity, and the horizontal axis implies

which has high cosine similarity.

the values of the collaborative filtering. We focused on two

Table 5 shows the distribution of results of the collabora-

users, F and G for user E. Both of them has high cosine sim-tive filtering. The value of the collaborative filtering should

ilarity which are over 0.6, but the values of the collaborative

be between -1 to 1. On this method, the values was calcu-

filtering are 0 and 0.54.

lated by the answers of the questions about wedding planningFirstly, we compared the posts of the users E and F. The

when users create the accounts on this wedding community

post of the user E describes the cousins’ impressive weddingsite. The high value means the users have similar wedding

with grooms’ tears. On the other hand, the post of the user F

planning. On this evaluation, we calculated 435 combina-

describes how cousins’ wedding was organized. Even though,tions of 30 users. The average value of all combinations iscommon words were used on their posts, the meanings of the

0.304. We confirm that many users have similar wedding

sentence and the topics were diﬀerent.

Figure 3 Distribution of the cosine similarity and the collaborative filtering E, H〜P

Secondly, we compared the posts of the users E and G.

Figure 4 Distribution of the cosine similarity and the collaborative filtering E, K, M〜O

collaborative filtering.

The post of the user E was mentioned above. The post of

In the future, we plan to enhance the proposed method

the user G describes the cousins’ wedding with tears because

based on the experimental results, and evaluate the eﬀects

of the letter that is about grand mother who was gone. Theseof user recommendation. Furthermore, we will extract theposts of them mentioned about the same type of wedding,relationships between users by constructing the matrix based

the cousins’ wedding with tears, even though the content ofon user behavior as our previous work. [8].

posts are a little diﬀerent.
As a result, we found that only calculating the cosine similarity is not eﬀective to detect the similar comment. However, we found that calculating both the cosine similarity andAcknowledgment
This work was partially supported by JSPS KAKENHI
Grant Numbers 15K00162, 16H01722.

the collaborative filtering is eﬀective. Therefore, these two
methods can help us to detect users’ similar comments toevoke communication among the users. Through this result,
we still need to evaluate other situation of users with otherusers’ axises, marriage status.
Fig. 4 shows the distribution of the cosine similarity andthe collaborative filtering for 5 users, E, K, M, N, and O
briefly. We could find several users who are especially similar to them such as users Q and R. As a future work, we areplanning to propose methods for clustering with the cosine
similarity and the collaborative filtering.

5. Conclusion
In this paper, we proposed an active communication mechanism for a wedding community site. This mechanism recommends 1) users who are supposed to evoke the communication, and 2) their comments. To detect users, this mechanism
classifies all users in 3 axises, which are “Static Profile Information”, ”Marriage Status”, and ”Active Profile Information”. Then, we calculated the similarities between users byusing the cosine similarity. To extract comments which wereposted on the wedding community site by the recommended
users, this mechanism detects the most related comments.
Finally, we evaluated the user characteristic extraction fromposts by comparing tf-idf methods and evaluated the similarity calculation methods with the cosine similarity and theReferences[1] Isaac, R. M. and Walker, “J. M., COMMUNICATION
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