recommendation system for online social network

103 Pages·2006·2.34 MB·English

by  Miss Had To

Master Thesis
Software Engineering
Thesis no: MSE-2006:11
July 2006

RECOMMENDATION SYSTEM FOR ONLINE SOCIAL
NETWORK

Katarzyna Musiał

School of Engineering
Blekinge Institute of Technology
Box 520
SE – 372 25 Ronneby
Sweden
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This thesis is submitted to the School of Engineering at Blekinge Institute
of Technology in partial fulfilment of the requirements for the degree of Master
of Science in Software Engineering. The thesis is equivalent to 20 weeks of full time
studies.

Contact Information
Author:
Katarzyna Musiał
E–mail:
ABSTRACT

Although there has been much work done
in the industry and academia on developing the theory
and application of social networks as well as
recommender systems, the relation between these
research areas is still unclear. An innovative idea,
which enables to integrate these areas, and applies
recommendation systems to the online social network
systems, is proposed in this thesis. Recommendation
systems for social networks differ from the typical
kinds of recommendation solutions, since they
suggest human beings to other ones rather than
inanimate goods. Thus, conventional recommendation
methods should be enhanced by social features
of the networks and their members. This thesis
presents the result of the study on
the recommendation framework for virtual
communities. It also contains an overview of recent
approaches to recommendation systems and social
networks, as well as description of the online social
network systems.

Keywords: Social Networks, Online Social Network
Systems, Personalized Recommender System
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CONTENTS
1 INTRODUCTION........................................................................................................................... 1
1.1 BACKGROUND AND MOTIVATION................................................................................................ 1
1.2 RESEARCH QUESTIONS AND OBJECTIVES................................................................................... 1
1.3 OUTLINE OF THESIS ..................................................................................................................... 2
2 RECOMMENDER SYSTEMS...................................................................................................... 3
2.1 BACKGROUND ............................................................................................................................... 3
2.2 DEFINITION OF RECOMMENDER SYSTEMS ................................................................................. 4
2.3 GOALS OF RECOMMENDER SYSTEMS ......................................................................................... 5
2.4 CATEGORIES OF RECOMMENDER SYSTEMS ............................................................................... 6
2.4.1 TAXONOMIES OF RECOMMENDER SYSTEMS............................................................................... 6
2.4.2 SIMPLIFIED APPROACHES.......................................................................................................... 10
2.4.3 DEMOGRAPHIC FILTERING ........................................................................................................ 11
2.4.4 COLLABORATIVE FILTERING..................................................................................................... 11
2.4.5 CONTENT–BASED FILTERING.................................................................................................... 13
2.4.6 HYBRID METHOD ...................................................................................................................... 14
3 ONLINE SOCIAL NETWORKS................................................................................................ 15
3.1 BACKGROUND ............................................................................................................................. 15
3.2 SOCIAL NETWORKS .................................................................................................................... 16
3.3 ONLINE SOCIAL NETWORKS...................................................................................................... 20
3.4 FUNCTIONS OF ONLINE SOCIAL NETWORK SYSTEMS ............................................................. 21
3.5 FEATURES OF SOCIAL NETWORKS ............................................................................................ 25
3.6 SOCIAL CAPITAL......................................................................................................................... 26
3.7 SOCIAL CAPITAL IN ONLINE SOCIAL NETWORK SYSTEMS..................................................... 28
3.8 SOCIAL NETWORK ANALYSIS .................................................................................................... 29
4 RECOMMENDER SYSTEM FOR ONLINE SOCIAL NETWORK ..................................... 31
4.1 PROBLEM DESCRIPTION............................................................................................................. 31
4.2 REQUIREMENTS AND PREREQUISITES OF SYSTEM................................................................... 32
4.3 THE CONCEPT OF RECOMMENDATION FOR SOCIAL NETWORKS ........................................... 33
4.4 USER PROFILE............................................................................................................................. 33
4.4.1 DEMOGRAPHIC COMPONENT .................................................................................................... 34
4.4.2 INTEREST COMPONENT ............................................................................................................. 38
4.4.3 PREFERRED COMPONENT .......................................................................................................... 41
4.4.4 SEARCH COMPONENT................................................................................................................ 41
4.4.5 ACTIVITY COMPONENT ............................................................................................................. 43
4.4.6 RELATIONSHIP COMPONENT ..................................................................................................... 44
4.5 RECOMMENDATION PROCESS.................................................................................................... 44
4.6 DATA PREPARATION ................................................................................................................... 46
4.7 PRELIMINARY FILTERING .......................................................................................................... 48
4.8 FINAL SIMILARITY BETWEEN USERS ........................................................................................ 48
4.8.1 DIRECT SIMILARITY .................................................................................................................. 49
4.8.1.1 Straight Comparison of Pair of Attributes ............................................................................. 50
4.8.1.2 Hierarchies ............................................................................................................................. 52
4.8.2 SEARCH MATCHING................................................................................................................... 54
4.8.3 COMPLEMENTARY OF RELATIONSHIP INITIATION .................................................................... 56
4.8.3.1 Assign users to specific class of initiators.............................................................................. 57
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4.8.3.2 Assign users to specific class of responders .......................................................................... 60
4.8.4 ACTIVITY OF THE USER............................................................................................................. 61
4.8.5 STRENGTH OF THE RELATIONSHIP ............................................................................................ 63
4.8.5.1 Simple Strength of Relationships........................................................................................... 64
4.8.5.2 Recurrent Strength of Relationships ...................................................................................... 67
4.9 SOCIAL FILTERING ..................................................................................................................... 67
4.10 SUMMARY.................................................................................................................................. 70
5 CHALLENGES............................................................................................................................. 71
5.1 ADJUSTMENT OF WEIGHTS........................................................................................................ 71
5.2 RULES .......................................................................................................................................... 71
6 CASE STUDY ............................................................................................................................... 74
6.1 DATA ABOUT USERS ................................................................................................................... 74
6.2 PRELIMINARY FILTERING.......................................................................................................... 77
6.3 DIRECT SIMILARITY ................................................................................................................... 77
6.4 SEARCH MATCHING .................................................................................................................... 78
6.5 COMPLEMENTARY OF RELATIONSHIP....................................................................................... 78
6.6 ACTIVITY OF THE USER .............................................................................................................. 78
6.7 STRENGTH OF RELATIONSHIP.................................................................................................... 79
6.8 FINAL SIMILARITY BETWEEN USERS ......................................................................................... 80
7 FUTURE WORK.......................................................................................................................... 82
8 CONCLUSIONS ........................................................................................................................... 83
9 REFERENCES.............................................................................................................................. 85
APPENDIX A – ABBREVIATIONS................................................................................................. 89
APPENDIX B – NOTATION............................................................................................................. 90
APPENDIX C – POLISH SUMMARY ............................................................................................. 92
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LIST OF FIGURES
FIGURE 2.1 LEVELS OF THE PERSONALIZATION IN THE RECOMMENDER SYSTEMS........................ 4
FIGURE 2.2 THE EXAMPLE OF TAXONOMY OF THE RECOMMENDER SYSTEMS [ADTU05] ............. 7
FIGURE 2.3 THE EXAMPLE OF TAXONOMY OF THE RECOMMENDER SYSTEMS [BUR02] ................ 7
FIGURE 2.4 THE EXAMPLE OF TAXONOMY OF THE RECOMMENDER SYSTEMS [MOLO03]............. 9
FIGURE 2.5 THE EXAMPLE OF TAXONOMY OF THE RECOMMENDER SYSTEMS [SAKO01] ............. 9
FIGURE 2.6 THE CLASSIFICATION OF THE RECOMMENDER SYSTEMS PRESENTED IN THE MASTER
THESIS ............................................................................................................................... 10
FIGURE 3.1 MEMBERS OF THE SEMANTIC WEB COMMUNITY AND THEIR ASSOCIATION WITH
RESEARCH INTERESTS [STA05] .......................................................................................... 17
FIGURE 3.2 THE DIVISION OF SOCIAL NETWORKS BASED ON THE TYPE OF THE RELATIONSHIP ... 18
FIGURE 3.3 THE EXAMPLE OF RELATIONSHIPS BETWEEN PEOPLE WORKING IN A COMPANY....... 19
FIGURE 3.4 THE DIVISION OF SOCIAL NETWORKS BASED ON THE TYPE OF THE COMMUNICATION
CHANNEL........................................................................................................................... 19
FIGURE 3.5 THE TAXONOMY OF CSSN...................................................................................... 20
FIGURE 3.6 SOCIAL FEATURES OF RELATIONSHIPS IN A SOCIAL NETWORK................................. 21
FIGURE 3.7 AN EXAMPLE OF THE ONLINE SOCIAL NETWORK SYSTEM ........................................ 22
FIGURE 3.8 MAIN FUNCTIONS OF THE ONLINE SOCIAL NETWORKS SYSTEMS.............................. 23
FIGURE 3.9 THE PROCESS OF STARTING NEW RELATIONSHIP ..................................................... 24
FIGURE 3.10 THE PROCESS OF MAINTENANCE OF THE RELATIONSHIP ........................................ 24
FIGURE 3.11 THE WAYS OF STIMULATING THE SOCIAL CAPITAL ................................................ 27
FIGURE 3.12 SOCIAL CAPITAL OF USER X GROUPED IN COMPONENTS......................................... 28
FIGURE 4.1 ONLINE AND OFFLINE ELEMENTS OF RECOMMENDATION PROCESS.......................... 33
FIGURE 4.2 USER DATA GROUPED IN PROFILE COMPONENTS ..................................................... 34
FIGURE 4.3 NAME ELEMENT AVAILABLE IN MYSPACE NETWORK ............................................... 35
FIGURE 4.4 BASIC INFO ELEMENT AVAILABLE IN MYSPACE NETWORK...................................... 36
FIGURE 4.5 BACKGROUND & LIFESTYLE ELEMENT AVAILABLE IN MYSPACE NETWORK.............. 37
FIGURE 4.6 DEMOGRAPHIC ATTRIBUTES USEFUL FROM RECOMMENDATION POINT OF VIEW ...... 38
FIGURE 4.7 INTEREST & PERSONALITY ELEMENT AVAILABLE IN MYSPACE NETWORK................. 39
FIGURE 4.8 INTEREST ELEMENT AVAILABLE IN FRIENDSTER NETWORK ..................................... 39
FIGURE 4.9 INTEREST ATTRIBUTES USEFUL FROM RECOMMENDATION POINT OF VIEW ............... 40
FIGURE 4.10 THE HIERARCHY OF THE INTEREST ATTRIBUTES.................................................... 40
FIGURE 4.11 PREFERRED ATTRIBUTES USEFUL FROM RECOMMENDATION POINT OF VIEW ......... 41
FIGURE 4.12 SIMPLE SEARCH AVAILABLE IN FRIENDSTER NETWORK......................................... 42
FIGURE 4.13 ADVANCED SEARCH AVAILABLE IN FRIENDSTER NETWORK .................................. 42
FIGURE 4.14 SEARCH ATTRIBUTES USEFUL FROM RECOMMENDATION POINT OF VIEW ............... 43
FIGURE 4.15 ACTIVITY ATTRIBUTES USEFUL FROM RECOMMENDATION POINT OF VIEW.............. 43
FIGURE 4.16 RELATIONSHIP ATTRIBUTES USEFUL FROM RECOMMENDATION POINT OF VIEW ..... 44
FIGURE 4.17 THE PROCESS OF RECOMMENDATION FOR USER X IN A SOCIAL NETWORK ............. 46
FIGURE 4.18 CLASSES OF VARIABLES USED IN CALCULATION OF DIRECT SIMILARITY FUNCTION
.......................................................................................................................................... 50
FIGURE 4.19 STRAIGHT COMPARISON OF PAIRS OF ATTRIBUTES ................................................ 51
FIGURE 4.20 EXAMPLE OF HIERARCHY OF THE SPORTS FOR DATA FROM TABLE 4.3 .................. 53
FIGURE 4.21 SEARCHES MADE BY USER X.................................................................................. 55
FIGURE 4.22 THE EXAMPLE OF VIRTUAL COMMUNITY............................................................... 56
FIGURE 4.23 SENT INVITATIONS AND RESPONSES TO THESE INVITATIONS IN THE COMMUNITY
FROM FIGURE 4.22 ............................................................................................................ 57
FIGURE 4.24 THE CREATION OF THE REQUIRED INTERVALS ....................................................... 58
FIGURE 4.25 THE CREATION OF LEVELS OF INITIATORS ............................................................. 58
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FIGURE 4.26 THE LEVELS OF INITIATORS – EXAMPLE ................................................................ 58
FIGURE 4.27 THE CREATION OF LEVELS OF INITIATORS BY UTILIZING STANDARD DEVIATION ... 59
FIGURE 4.28 THE LEVELS OF INITIATORS – EXAMPLE STANDARD DEVIATION ............................ 60
FIGURE 4.29 THE ACTIVITIES WITHIN SOCIAL NETWORK ........................................................... 61
FIGURE 4.30 THE THEORETICAL RELATIONSHIPS OF USER Y ...................................................... 63
FIGURE 4.31 THE EXAMPLE OF VIRTUAL COMMUNITY............................................................... 65
FIGURE 4.32 EXAMPLES OF SOCIAL NETWORKS WITH CALCULATED SP(X) AND C(Y�X) FOR
EACH USER, e=1................................................................................................................. 68
FIGURE 4.33 THE ELEMENTS OF SOCIAL FILTERING ................................................................... 69
FIGURE 5.1 USER DATA GROUPED IN PROFILE COMPONENTS ..................................................... 72
FIGURE 6.1 NEW USER JOINS TO THE EXISTING COMMUNITY ..................................................... 74
FIGURE 6.2 USER PROFILE USED IN THE EXAMPLE ..................................................................... 74
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LIST OF TABLES
TABLE 2.1 THE DIVISION OF RECOMMENDATION TECHNIQUES [BUR02]...................................... 8
TABLE 4.1 THE HIERARCHY AND EXAMPLES OF INTERESTS ....................................................... 41
TABLE 4.2 OCCUPATIONS OF PERSONS' X AND Y........................................................................ 47
TABLE 4.3 COMPARISON OF USERS' X AND Y INTERESTS............................................................. 52
TABLE 4.4 POINTS ASSIGNED FOR LEVELS FOR SPECIFIC HIERARCHICAL INTERESTS.................. 53
TABLE 4.5 POINTS ASSIGNED FOR LEVELS FOR SPECIFIC HIERARCHICAL INTERESTS.................. 54
TABLE 4.6 COMPARISON OF USERS' PLACES OF ORIGIN.............................................................. 54
TABLE 4.7 DEMOGRAPHIC COMPONENT OF USER Y .................................................................... 55
TABLE 4.8 ATTRIBUTES AND THEIR FREQUENCY OF OCCURRENCE IN SEARCH COMPONENT OF
USER X............................................................................................................................... 55
TABLE 4.9 SENT, RECEIVED INVITATIONS AND RESPONSES TO INVITATIONS WITHIN COMMUNITY
FROM FIGURE 4.22 ............................................................................................................ 57
TABLE 4.10 INITIATIONS LEVELS ASSIGNED TO USERS BASED ON UTILIZING DIFFERENT METHODS
.......................................................................................................................................... 60
TABLE 4.11 RESPONSES LEVELS ASSIGNED TO USERS................................................................ 60
TABLE 4.12 FREQUENCY OF CHANGING PHOTO ALBUM IN THE THEORETICAL COMMUNITY....... 62
TABLE 4.13 THE WAYS AND FREQUENCY OF EXCHANGING INFORMATION BETWEEN USER X AND
THEIR ALTERS.................................................................................................................... 65
TABLE 4.14 THE WAYS AND FREQUENCY OF EXCHANGING INFORMATION BETWEEN USER Y AND
THEIR ALTERS.................................................................................................................... 65
TABLE 4.15 THE WAYS AND FREQUENCY OF EXCHANGING INFORMATION BETWEEN USER V AND
THEIR ALTERS.................................................................................................................... 66
TABLE 4.16 THE WAYS AND FREQUENCY OF EXCHANGING INFORMATION BETWEEN USER U AND
THEIR ALTERS.................................................................................................................... 66
TABLE 4.17 THE WAYS AND FREQUENCY OF EXCHANGING INFORMATION BETWEEN USER Z AND
THEIR ALTERS.................................................................................................................... 66
TABLE 6.1 DATA ABOUT USER X................................................................................................ 75
TABLE 6.2 DATA ABOUT MEMBERS OF THE NETWORK............................................................... 76
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Relationships matter – ICk4U

I dedicate this thesis
to my parents and sister
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Recommendation system for online social network
1 Introduction
This is an introductory chapter of the thesis. First section enumerates the reasons why
the research on recommender systems (RS) in online social networks (OSN) should be done.
Second section presents not only the aim and the objectives of the thesis but also research
questions for which the answers are presented in the further part of this thesis. Finally, last
section outlines the remainder of the thesis.
1.1 Background and Motivation
The idea of both online social networks and recommender systems was developed by
many researches. All existing recommendation methods suggest products or services
to people [AdTu05, MoLo03, SaKo01]. Whereas method presented in this thesis recommends
one human being to another. The whole idea of applying recommender systems in social
network is innovative, thus many new challenges have occurred. Building such a framework
requires integrating two separate branches of knowledge: computer science and sociology.
This makes work even more interesting because enables to create interdisciplinary groups
in which people with different backgrounds can cooperate with each others.
On one hand, when we recommend one person to another, we should have deep
knowledge about algorithms that can be utilized and pick the appropriate one. Usually,
the chosen method must be further tailored to the specific needs of the system. Additionally, it
is common practice that the creators of RS integrate many different recommendation methods
because some techniques complement other ones. In other words, one technique usually copes
with the shortcoming of other one and this relationship is bidirectional.
Nevertheless, not only the knowledge about recommender systems is required.
On the other hand, we should know a social behaviour of people who create the network
and be aware of their preferences. In the recommender system for online social network not
only the needs and preferences of a person who expects some suggestions should be
considered, but also the expectations of a person who will be suggested. This factor makes
the whole problem even more complicated but also more fascinating.
1.2 Research Questions and Objectives
The main aim of this thesis is to create the recommendation method that will support
the evolution of online social networks known as virtual communities.
The objectives of the thesis are as follows:
• Gain knowledge about the subject (recommender systems and social networks)
through research in literature
• Create the classification of existing social networks
• Build the user profile in which different kind of data will be gathered
• Present the way in which different sources of data can be integrated
• Define the recommendation process for social network that concerns
the preferences and human limitations of people who will be recommended
The thesis addressing following research questions:
• What kinds of recommendation methods do exist?
1