Recommendation has become an important mobile application on location-based social networks (LBSNs), especially when users travel to a new place far away from their home. Compared to traditional recommender systems, this type of recommendation is very challenging. A user on geo-social network usually visits only a very limited number of spatial items (points of interest), resulting in sparse user-item matrix. As most users tend to visit the spatial items nearby their homes, the user-item matrix will become even sparser when users travel to a distant place. Another major challenge is that, users' interests and behavior patterns tend to vary dramatically across different time period and different geographical regions. In this chapter, we focus on effective spatial item recommendation by exploiting both spatial and temporal information on geo-social networks. To solve the sighted challenges, we propose ST-SAGE, a spatial- temporal sparse additive generative (SAGE) model for spatial item recommendation. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns of spatial items and the content of spatial items. To further alleviate the data sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called spatialpyramid. To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments, and the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency and online recommendation efficiency.

Chapter Contents:

• 9.1 Introduction
• 9.2 Preliminaries about SAGE
• 9.3 Spatial – temporal SAGE model
• 9.3.1 Problem definitions
• 9.3.2 Model description
• 9.3.3 Model inference
• 9.3.4 Spatial smoothing
• 9.3.5 Parallel implementation
• 9.4 Spatial item recommendation using ST-SAGE
• 9.5 Experiments
• 9.5.1 Experimental settings
• 9.5.1.1 Datasets
• 9.5.1.2 Comparative approaches
• 9.5.1.3 Evaluation methods
• 9.5.2 Recommendation effectiveness
• 9.5.2.1 Results and analysis
• 9.5.2.2 Impact of different factors
• 9.5.3 Recommendation efficiency
• 9.5.3.1 Model training efficiency
• 9.5.3.2 Online recommendation efficiency
• 9.6 Related work
• 9.7 Conclusion
• References

Inspec keywords: inference mechanisms; mobile computing; recommender systems; data analysis; social networking (online); geographic information systems

Other keywords: big geo-social networking data; spatial index structure; ST-SAGE; temporal information; spatiotemporal recommendation; model inference algorithm; mobile application; spatial-temporal sparse additive generative model; spatial information; geo-social network; recommender systems; spatialpyramid; online recommendation efficiency; recommendation effectiveness; sparse user-item matrix; GraphLab framework; location-based social networks; effective spatial item recommendation

Subjects: Information networks; Data handling techniques; Knowledge engineering techniques; Geography and cartography computing; Mobile, ubiquitous and pervasive computing