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Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location Based Community Trends Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location Based Community Trends Shaymaa Khater Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science and Applications Denis Gracanin,ˇ Chair Hicham G. Elmongui, Co-Chair Andrea L. Kavanaugh James D. Ivory Kristina Lerman 9 November 2015 Blacksburg, VA Keywords: Social Networks, Microblogs, Recommendation Systems Copyright 2015, Shaymaa Khater Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location Based Community Trends Shaymaa Khater (ABSTRACT) Online social networks are experiencing an explosive growth in recent years in both the number of users and the amount of information shared. The users join these social networks to connect with each other, share, find content and disseminate information by sending short text messages in near realtime. As a result of the growth of social networks, the users are often experiencing information overload since they interact with many other users and read ever increasing content volume. Thus, finding the “matching” users and content is one of the key challenges for social networks sites. Recommendation systems have been proposed to help users cope with information overload by predicting the items that a user may be interested in. The users’ preferences are shaped by personal interests. At the same time, users are affected by their surroundings, as determined by their geographically located communities. Accordingly, our approach takes into account both personal interests and local communities. We first propose a new dynamic recommendation system model that provides better customized content to the user. That is, the model provides the user with the most important tweets according to his individual interests. We then analyze how changes in the surrounding environment can affect the user’s expe- rience. Specifically, we study how changes in the geographical community preferences can affect the individual user’s interests. These community preferences are generally reflected in the local- ized trending topics. Consequently, we present TrendFusion, an innovative model that analyzes the trends propagation, predicts the localized diffusion of trends in social networks and recommends the most interesting trends to the user. Our performance evaluation demonstrate the effectiveness of the proposed recommendation system and shows that it improves the precision and recall of identifying important tweets by up to 36% and 80%, respectively. Results also show that TrendFu- sion accurately predicts places in which a trend will appear, with 98% recall and 80% precision. To my great parents. To my wonderful husband, Ahmad. To my lovely kids, Ali and Abdullah. iii Acknowledgments First and foremost, I thank Allah for the numerous blessings He has bestowed upon me throughout my dissertation journey. I would like to express my deepest gratitude to my advisor, Professor Denis Gracanin, for his excellent guidance and persistent support during my PhD study. Prof. Gracanin provided me the freedom through my Ph.D. to explore various research problems where he always gave stimulating and fruitful discussions. I have always enjoyed our weekly meeting in which he has helped me in all the aspects of this research. I have also benefited tremendously from his feedback, ideas, and criticism on my research, writing, and presentation skills. I am deeply indebted to my Co-advisor, Professor Hicham Elmongui. From the first days of my PhD program in Egypt, his moral support, and continuous encouragement have been of extreme value for helping me getting through my PhD studies. He inspired me with his wisdom, hard work, and attention to details. He was always there to help and to give sincere advice whenever I need, not only on the academic level, but on the personal level as well. I am forever grateful for his support. In addition, I would like to thank Professor Andrea Kavanaugh, Professor James Ivory, and Pro- fessor Kristina Lerman for serving in my Ph.D. committee and providing constructive feedbacks on my dissertation. My deepest gratitude goes to my husband, Ahmad, and to our kids, Ali and Abdullah. Ahmad’s unlimited support, encouragement and understanding was always the incentive to complete my iv study. He was always supportive for me in the tough times, and was always keen to prioritize our family over himself. Without him, it would have been impossible for me to finish the five years of this long journey. I am grateful to my children, Ali and Abdullah. They have been told “Not right now, mommy’s working” often – too often. I am grateful for them for being understandable when I was so tired or busy to give them attention. Their smiles always give me the light and the power to achieve. I am thankful to my parents, who spent endless efforts, all over the years, providing me with all the love, help, and care. They always gave without return, and their sacrifices are everlasting. v Contents Contents vi List of Figures xi List of Tables xiv 1 Introduction1 1.1 Recommendation Systems..............................2 1.2 Recommendation Systems in Social Networks....................4 1.3 Challenges in Recommendation Systems.......................5 1.4 The User as a Part Of Different Communities....................6 1.5 Twitter: A Case Study................................7 1.6 Trending Topics in Twitter..............................9 1.7 Motivation....................................... 10 1.8 Proposed Approach.................................. 12 1.9 Dissertation Organization............................... 14 vi 2 Related Work 15 2.1 Recommendation Systems in Online Social Networks................ 16 2.1.1 Categorization by Approach......................... 17 2.1.2 Categorization by Objective......................... 21 2.1.3 Other Recommendation Systems....................... 25 2.2 Information and Influence Propagation in Social Networks............. 27 2.3 Trending Topics in Social Networks......................... 28 2.4 Topic Modeling.................................... 31 2.5 Event Detection.................................... 32 3 Problem Description 35 4 Tweets Analysis Subsystem 39 4.1 Introduction...................................... 39 4.2 Problem Description................................. 40 4.3 Topic Extraction.................................... 42 4.4 Tweets Pooling.................................... 42 4.5 Dynamic Level of Interest.............................. 43 4.6 Personalized Tweet Recommendation........................ 45 4.6.1 Personalized Social Features......................... 46 4.6.2 Explicit Features............................... 46 4.7 Experimental Results................................. 47 vii 4.7.1 Data Collection................................ 47 4.7.2 Dataset and Preprocessing.......................... 50 4.7.3 Tweets Pooling................................ 51 4.7.4 Evaluating Topic Models........................... 52 4.7.5 Calculating the Dynamic Level of Interest.................. 54 4.7.6 Personalized Recommender Model..................... 54 4.7.7 Dynamic LoI and Other Features Effect................... 55 4.8 Discussion....................................... 55 4.8.1 Tweets Pooling Effect............................ 58 4.8.2 Number of Topics Variation Effect...................... 60 4.9 Summary....................................... 61 5 Trends Analysis Subsystem 63 5.1 Introduction...................................... 63 5.2 TrendFusion Framework............................... 64 5.3 TrendFusion Model.................................. 65 5.4 Generating the Hazard Rate Graph.......................... 66 5.5 TrendFusion Stages.................................. 67 5.5.1 Stage 1: Collect and Store Trending Topics Stream from Locations..... 68 5.5.2 Stage 2: Build Cascades........................... 68 5.5.3 Stage 3: Extract Parameters......................... 68 5.5.4 Stage 4: Model Learning/Using....................... 72 viii 5.6 Snowball Cascade Model............................... 72 5.6.1 SC Model Definition............................. 74 5.6.2 GT Model Definition............................. 75 5.7 Evaluation....................................... 75 5.7.1 Trending Topics Dataset........................... 75 5.7.2 Using TrendFusion Framework....................... 78 5.7.3 Experiments................................. 79 5.7.4 Results and Discussion............................ 81 5.8 Summary....................................... 87 6 Personalized Recommendation 88 6.1 Introduction...................................... 88 6.2 Multilevel Trends Filtering.............................. 89 6.3 Tweets and Trends Recommendation......................... 90 6.4 Effect of Topic Modeling on Recommendation.................... 92 6.5 TrendFusion System................................. 92 6.6 Summary....................................... 95 7 TrendFusion User Study 97 7.1 Purpose........................................ 97 7.2 Method........................................ 97 7.2.1 Participants.................................. 97 ix 7.2.2 Procedure................................... 98 7.3 Users Tasks...................................... 98 7.4 Results and Discussion................................ 99 8 Conclusion 102 Bibliography 106 Appendix A User Study 122 A.1 TrendFusion System Setup.............................
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