Spatio-Temporal Recommendation in Social Media

Users’ behaviors in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public’s attention at that time). In this chapter, we focus on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. TCAM simultaneously models the topics related to users’ intrinsic interests and the topics related to temporal context, and then combines the influences from the two factors to model user behaviors in a unified way. To further improve the performance of TCAM, an item-weighting scheme is proposed to enable TCAM to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Extensive experiments have been conducted to evaluate the performance of TCAM on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of the TCAM models.

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, Topic-Region Model (TRM), to simultaneously discover the semantic, temporal and spatial patterns of users’ check-in activities, and to model their joint effect on users’ decision-making for selection of POIs to visit. To demonstrate the applicability and flexibility of TRM, we investigate how it supports two recommendation scenarios in a unified way, i.e., hometown recommendation and out-of-town recommendation. TRM effectively overcomes the data sparsity by the complementarity and mutual enhancement of the diverse information associated with users’ check-in activities (e.g., check-in content, time and location) in the processes of discovering heterogeneous patterns and producing recommendation. To support real-time POI recommendation, we further extend the TRM model to an online learning model TRM-Online to track changing user interests and speed up the model training. We conduct extensive experiments to evaluate the performance of our proposals on two real-world datasets including recommendation effectiveness, overcoming cold-start problem and model training efficiency. The experimental results demonstrate the superiority of our TRM models, especially the TRM-Online, compared with the state-of-the-art competitive methods, by making more effective and efficient mobile recommendations. Besides, we study the importance of each type of patterns in the two recommendation scenarios, respectively, and find that exploiting temporal patterns is most important for the hometown recommendation scenario, while the semantic patterns play a dominant role in improving the recommendation effectiveness for out-of-town users.