Yin / Cui Spatio-Temporal Recommendation in Social Media

1. Auflage 2016
ISBN: 978-981-10-0748-4
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 122 Seiten, eBook

Reihe: SpringerBriefs in Computer Science

This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users’ behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users’ behaviors, user interest drift over geographical regions, data sparsity and cold start.  Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.  

Zielgruppe

Research

Autoren/Hrsg.

Yin, Hongzhi

Cui, Bin

Inhaltsverzeichnis

1;Preface;7
2;Acknowledgments;8
3;Contents;9
4;1 Introduction;12
4.1;1.1 Background;12
4.2;1.2 The Research Issues and Challenges;14
4.3;1.3 Overview of the Book;15
4.4;1.4 Literature and Research Review;17
4.4.1;1.4.1 Traditional Context-Aware Recommendation;17
4.4.2;1.4.2 Temporal Recommendation;18
4.4.3;1.4.3 Spatial Item Recommendation;19
4.4.4;1.4.4 Real-Time Recommendation;20
4.4.5;1.4.5 Online Recommendation Efficiency;21
4.5;References;23
5;2 Temporal Context-Aware Recommendation;27
5.1;2.1 Introduction;27
5.2;2.2 User Rating Behavior Modeling;30
5.2.1;2.2.1 Notations and Definitions;30
5.2.2;2.2.2 Temporal Context-Aware Mixture Model;32
5.2.3;2.2.3 Model Inference;34
5.2.4;2.2.4 Discussion About TCAM;36
5.2.5;2.2.5 Item-Weighting for TCAM;37
5.3;2.3 Temporal Recommendation;39
5.4;2.4 Experiments;40
5.4.1;2.4.1 Datasets;40
5.4.2;2.4.2 Comparisons;41
5.4.3;2.4.3 Evaluation Methodology;42
5.4.4;2.4.4 Recommendation Effectiveness;44
5.4.5;2.4.5 Temporal Context Influence Study;45
5.4.6;2.4.6 User Profile Analysis;47
5.5;2.5 Summary;48
5.6;References;48
6;3 Spatial Context-Aware Recommendation;50
6.1;3.1 Introduction;50
6.2;3.2 Location-Content-Aware Recommender System;53
6.2.1;3.2.1 Preliminary;53
6.2.2;3.2.2 Model Description;54
6.2.3;3.2.3 Model Inference;58
6.2.4;3.2.4 Online Recommendation;61
6.3;3.3 Experiments;61
6.3.1;3.3.1 Datasets;62
6.3.2;3.3.2 Comparative Approaches;63
6.3.3;3.3.3 Evaluation Methods;65
6.3.4;3.3.4 Recommendation Effectiveness;66
6.3.5;3.3.5 Local Preference Influence Study;68
6.3.6;3.3.6 Analysis of Latent Topic;70
6.4;3.4 Summary;71
6.5;References;71
7;4 Location-Based and Real-Time Recommendation;73
7.1;4.1 Introduction;74
7.1.1;4.1.1 Joint Modeling of User Check-In Behaviors;75
7.1.2;4.1.2 Real-Time POI Recommendation;76
7.2;4.2 Joint Modeling of User Check-In Activities;77
7.2.1;4.2.1 Preliminary;78
7.2.2;4.2.2 Model Structure;79
7.2.3;4.2.3 Generative Process;82
7.2.4;4.2.4 Model Inference;83
7.3;4.3 Online Learning for TRM;85
7.3.1;4.3.1 Feasibility Analysis;85
7.3.2;4.3.2 Online Learning Algorithm;86
7.4;4.4 POI Recommendation Using TRM;91
7.4.1;4.4.1 Fast Top-k Recommendation Framework;93
7.4.2;4.4.2 Addressing Cold-Start Problem;94
7.5;4.5 Experiments;94
7.5.1;4.5.1 Datasets;94
7.5.2;4.5.2 Comparative Approaches;96
7.5.3;4.5.3 Evaluation Methods;97
7.5.4;4.5.4 Recommendation Effectiveness;98
7.5.5;4.5.5 Impact of Different Factors;100
7.5.6;4.5.6 Test for Cold-Start Problem;102
7.5.7;4.5.7 Model Training Efficiency;103
7.6;4.6 Summary;104
7.7;References;104
8;5 Fast Online Recommendation;107
8.1;5.1 Introduction;107
8.1.1;5.1.1 Parallelization;108
8.1.2;5.1.2 Nearest-Neighbor Search;108
8.2;5.2 Metric Tree;110
8.2.1;5.2.1 Branch-and-Bound Algorithm;111
8.3;5.3 TA-Based Algorithm;113
8.3.1;5.3.1 Discussion;115
8.4;5.4 Attribute-Pruning Algorithm;116
8.5;5.5 Experiments;119
8.5.1;5.5.1 Experimental Results;119
8.6;5.6 Summary;121
8.7;References;122

Über Autor(innen)

Dr. Hongzhi Yin has been an ARC DECRA fellow in the School of Information Technology and Electrical Engineering (ITEE), at The University of Queensland (UQ), and he received his PhD degree from Peking University in July 2014. His research interests include Recommender System and User Modeling, Social Media Mining and Management, Location-based Social Network Analysis, Deep Learning and Spatial Database.  Due to his great contributions to recommendation in social media, he was granted the Distinguished Doctor Degree Thesis Award of Peking University in 2014. Besides, he held the honors of outstanding graduate from Beijing provincial government of P.R. China. He was the winner of the National Scholarship from Ministry of Education of P.R. China in 2008 as well as the winner of National Graduate Scholarship from Ministry of Education of P.R. China in 2013. Dr. Yin has published over 30 related peer-reviewed publications in prestigious journals and conferences of the database, data mining and information retrieval fields, including SIGMOD, VLDB, KDD, ICDE, ACM Multimedia, CIKM, TOIS (ACM Transactions on Information Systems),  TKDD (ACM Transactions on Knowledge Discovery from Data), TIST (ACM Transactions on Intelligent Systems and Technology) and World Wide Web. He has served in the Technical Program Committee of various international conferences including IEEE International Conference on Data Science in Cyberspace 2016, WISE 2016&2015, APWEB 2016&2015, DEXA 2016&2015, WAIM 2016&2015. He has also serve as invited reviewers for several prestigious journals such as VLDB Journal, IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on the Web (TWeb), IEEE Transactions on Cybernetics, IEEE Transactions on Cloud Computing (TCC), Pervasive and Mobile Computing (PMC), New Review of Hypermedia and Multimedia, Frontiers of Computer Science (FCS), Journal of Image and Vision Computing, Knowledge-Based Systems, New Review of Hypermedia and Multimedia.
Prof. Bin Cui is a faculty member at the School of EECS and Vice Director of the Institute of Network Computing and Information Systems, at Peking University. He obtained his BSc from Xi'an Jiaotong University (Pilot Class) in 1996, and his PhD from the National University of Singapore in 2004. From 2004 to 2006, he worked as a Research Fellow in the Singapore-MIT Alliance. His research interests include database system architectures, query and index techniques, and big data management and mining. He has served in the Technical Program Committee of various international conferences including SIGMOD, VLDB, ICDE and KDD, and as Vice PC Chair of ICDE 2011, Demo CO-Chair for ICDE 2014, and as Area Chair of VLDB 2014. He is currently on the Editorial Board of VLDB Journal, Distributed and Parallel Databases Journal, Information Systems, and Frontier of Computer Science, and was an associate editor of IEEE Transactions on Knowledge and Data Engineering (TKDE, 2009-2013). He has received the Microsoft Young Professorship award (MSRA 2008) and the CCF Young Scientist award (2009). He is a senior member of IEEE, member of ACM and distinguished member of CCF.