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Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Overview -- 1.2 Backgrounds -- 1.2.1 Problem Description -- 1.2.2 User Behavior Analysis -- 1.2.3 Methodologies -- 1.3 Book Organization -- References -- 2 Understanding Human Mobility from Geographical Perspective -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Model -- 2.3.1 Gaussian Mixture Model -- 2.3.2 Genetic Algorithm Based Gaussian Mixture Model -- 2.4 Experiment -- 2.4.1 Setup and Metrics -- 2.4.2 Dataset -- 2.4.3 Results -- 2.5 Conclusion -- References -- 3 Understanding Human Mobility from Temporal Perspective -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Preliminaries -- 3.3.1 Empirical Data Analysis -- 3.3.2 Time Labeling Scheme -- 3.4 Method -- 3.4.1 Aggregated Temporal Tensor Factorization Model -- 3.4.2 Learning -- 3.4.3 Model Discussion -- 3.5 Experiment -- 3.5.1 Data Description and Experimental Setting -- 3.5.2 Performance Metrics -- 3.5.3 Baselines -- 3.5.4 Experimental Results -- 3.6 Conclusion -- References -- 4 Geo-Teaser: Geo-Temporal Sequential Embedding Rank for POI Recommendation -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Data Description and Analysis -- 4.3.1 Data Description -- 4.3.2 Empirical Analysis -- 4.4 Method -- 4.4.1 Temporal POI Embedding -- 4.4.2 Geographically Hierarchical Pairwise Ranking -- 4.4.3 Geo-Teaser Model -- 4.4.4 Learning -- 4.5 Experimental Evaluation -- 4.5.1 Experimental Setting -- 4.5.2 Performance Metrics -- 4.5.3 Model Comparison -- 4.5.4 Experimental Results -- 4.6 Conclusion -- References -- 5 STELLAR: Spatial-Temporal Latent Ranking Model for Successive POI Recommendation -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Data Description and Successive Check-in Analysis -- 5.3.1 Data Description -- 5.3.2 Successive Check-in Analysis -- 5.4 STELLAR Model -- 5.4.1 Time Indexing Scheme -- 5.4.2 Model Formulation.

This book systematically introduces Point-of-interest (POI) recommendations in Location-based Social Networks (LBSNs). Starting with a review of the advances in this area, the book then analyzes user mobility in LBSNs from geographical and temporal perspectives. Further, it demonstrates how to build a state-of-the-art POI recommendation system by incorporating the user behavior analysis. Lastly, the book discusses future research directions in this area. This book is intended for professionals involved in POI recommendation and graduate students working on problems related to location-based services. It is assumed that readers have a basic knowledge of mathematics, as well as some background in recommendation systems.

This timely text/reference presents the latest advances in various aspects of social media modeling and social media computing research. Gathering together superb research from a range of established international conferences and workshops, the editors coherently organize and present each of the topics in relation to the basic principles and practices of social media modeling and computing. Individual chapters can be also be used as self-contained references on the material covered. Topics and features: presents contributions from an international selection of preeminent experts in the field