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Enhancing Geo-Social Systems: Profiling, Ranking and Recommendation ENHANCING GEO-SOCIAL SYSTEMS: PROFILING, RANKING AND RECOMMENDATION A Dissertation by WEI NIU Submitted to the Office of Graduate and Professional Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Chair of Committee, James Caverlee Committee Members, Frank Shipman Krishna Narayanan Yoonsuck Choe Head of Department, Dilma Da Silva December 2017 Major Subject: Computer Engineering Copyright 2017 Wei Niu ABSTRACT The global sharing of fine-grained geo-spatial footprints – via smart mobile devices and social media services (e.g., Facebook, Twitter, Flickr) – is leading to the creation of a new class of geo-social systems. These systems promise new insights into the dynam- ics of human behavior and new intelligent location-aware applications, enabled by both geographic and social characteristics of their users. Yet, we are just beginning to under- stand these nascent systems. Indeed, there is a significant research gap in understanding, modeling, and leveraging geo-tagged user activities and in identifying the key factors that influence the success of new geo-social systems. Hence, this dissertation research seeks to enhance existing and future geo-social sys- tems through a systematic study of the intrinsic mutual reinforcement relationship between geography (geo) and user behaviors (social). In particular, we focus on three important scenarios: geo-social profiling, ranking, and recommendation. In summary, this disserta- tion makes three unique contributions: • The first contribution of this dissertation research lies in frameworks for profiling both users and locations in geo-social systems. We propose a framework to identify concep- tual communities and a smoothing-based approach that collectively balances the infor- mation from physical neighbors and conceptual community for estimating the hashtag distribution at a particular location. We further propose a location-sensitive folkson- omy construction framework and build high-quality tag profiles for users by identifying candidate tags from these location-sensitive folksonomies, and then employ a learning- to-rank approach for ordering these tags. • The second contribution of this dissertation research is a framework for location-sensitive topical expert identification and ranking in social media. Three of the key features ii of the proposed approach are: (i) a learning-based framework for integrating multiple user-based, content-based, list-based, and crowd-based factors impacting local exper- tise that leverages the fine-grained GPS coordinates of millions of social media users; (ii) a location-sensitive random walk that propagates crowd knowledge of a candidate’s expertise; and (iii) a comprehensive controlled study over AMT-labeled local experts on eight topics and in four cities. • In the third contribution, we create a new geo-social framework for effective person- alized image recommendation as part of the effort toward enhancing geo-social sys- tems. We propose Neural Personalized Ranking (NPR) – a personalized pairwise rank- ing model over implicit feedback datasets – that is inspired by Bayesian Personalized Ranking (BPR) and recent advances in neural networks. We further build an enhanced model which significantly boosts performance by augmenting the basic NPR model with multiple contextual preference clues derived from geographic features, user tags and visual factors. iii DEDICATION To my mother, my father, and Tian. iv ACKNOWLEDGMENTS First and foremost, I would like to express my deepest gratitude to my advisor Dr. James Caverlee, for his exceptional guidance, caring, patience, and providing me with an excellent resources and atmosphere for doing research. Back in 2012, Dr. Caverlee inspired me in the beginning with his personality and teaching and I strongly felt he is the professor to work with. During these years, he continuously fuels me with impetus, confidence and passion for research in everyday life. I’m always able to draw strength from his creative insights and wise guidance, making me less confused in envisioning the research problem and helping me avoid detours on my PhD pathway. I have also been continuously learning from his technical knowledge, critical thinking and professionalism through numerous weekly meetings, which significantly help developing my research at- titude and capability. Dr. Caverlee is not only a preeminent advisor in research, but also an important role model and life mentor for me. I’m thankful for his encouragement and optimism which helped me went through my hard times and influenced me to positively deal with difficulties. I would like to acknowledge with gratitude the love and caring from my family. Espe- cially my parents, for their unconditional support and selfless devotion to me. Thank you for providing me the great education and environment for me to grow up. I’m grateful for my father’s advice whenever I face some dilemma and for my mother’s loving care in all these years. Meanwhile, I would like to thank my girlfriend Tian. Words cannot describe how fortunate and joyful I am to have her in my life. She is always there cheer me up. Very grateful for having her accompany in all these years in a foreign land. I would like to thank my lab members – Haokai Lu, Cheng Cao, Hancheng Ge, Majid Alfifi, Zhijiao Liu, Haiping Xue, Henry Qiu, Xing Zhao, Parisa Kaghazgaran, Yin Zhang v and others who worked in the lab. Their dedication and perseverance kept motivating me in this 5-year journey. Thank you all and the best of luck in your future endeavors. Additionally I would like to thank former lab members Krishna Kymath, Zhiyuan Cheng, Kyumin Lee, Elham Kabiri and Yuan Liang. They helped me get familiar with the research areas, and showed me how to be a successful PhD student. I’m also grateful to Dr. Yoonsuck Choe, Dr. Krishna Narayanan and Dr. Frank Ship- man for generously being my committee members. Thank you for the constructive com- ments and feedback that help make my research work more polished and valuable. Thank for the time you devoted to me. Special thanks to Dr. Xia Hu, for attending my defense. vi CONTRIBUTORS AND FUNDING SOURCES Contributors This work was supported by a dissertation committee consisting of Professor James Caverlee and Frank Shipman, Yoonsuck Choe of the Department of Computer Science and Engineering and Professor Krishna Narayanan of the Department of Electrical and Computer Engineering. Funding Sources Graduate study was supported by a research assistantship from Texas A&M University. vii TABLE OF CONTENTS Page ABSTRACT ............................................................................... ii DEDICATION............................................................................. iv ACKNOWLEDGMENTS ................................................................ v CONTRIBUTORS AND FUNDING SOURCES ....................................... vii TABLE OF CONTENTS ................................................................. viii LIST OF FIGURES ....................................................................... xii LIST OF TABLES......................................................................... xiv 1. INTRODUCTION..................................................................... 1 1.1 Motivation ....................................................................... 1 1.2 Challenges ....................................................................... 2 1.3 Contributions .................................................................... 3 1.4 Dissertation Overview .......................................................... 6 2. RELATED WORK .................................................................... 9 2.1 Location and User Profiling .................................................... 9 2.2 Local Expert Detection ......................................................... 11 2.3 Personalized Geo-social Image Recommendation............................. 13 3. PROFILING: COMMUNITY-BASED GEOSPATIAL TAG ESTIMATION ...... 16 3.1 Introduction...................................................................... 16 3.2 Geospatial Tag Distribution Estimation ........................................ 18 3.2.1 Challenge 1: Finding Conceptual Communities ...................... 19 3.2.2 Challenge 2: Hashtag Distribution Estimation ........................ 22 3.3 Experimental Evaluation........................................................ 25 3.3.1 Dataset .................................................................. 25 3.3.2 Evaluating Hashtag Distribution Estimation .......................... 25 3.3.3 Conceptual Community Discovery .................................... 26 3.3.4 Evaluating the Community-based Approach .......................... 30 viii 3.3.5 Comparing Three Estimation Approaches ............................ 33 3.4 Summary ........................................................................ 36 4. PROFILING: LOCATION-SENSITIVE USER PROFILING ...................... 37 4.1 Introduction...................................................................... 37 4.2 Spatial Variation in Crowd-sourced Labels .................................... 39 4.2.1 How does distance impact tagging? ................................... 40 4.2.2 Are tags evenly distributed across locations? ......................... 41 4.2.3 Example location-sensitive relationships. ............................. 43 4.3 Location-Sensitive User Profiling .............................................. 43 4.3.1 Problem Statement ....................................................
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