Event-Based Probabilistic Embedding for POI Recommendation

Location-based social networks (LBSNs) have collected massive geo-tagged information, enabling the derivation of user preference for point of interests (POIs) in support of personalized recommendation. The existing embedding techniques deal with multiple factors by embedding a separate model for each factor. As a result, the interaction amongst various factors cannot be captured properly. In addition, we notice that the effectiveness of personalized recommendation is closely related to the current time and location. It is obvious that users would check into a POI which fits their interests, even if the current location is far away from the POI or the time is inappropriate. Therefore, it is necessary to recommend the right POI according to the time and geographic location of the user. In other words, it is necessary to predict the most likely visiting event, including users, POI, event time, and event location. In this paper, we propose a probabilistic embedding model called Topic And Region Embedding (TARE), which embeds events by simulating the users' decision-making process. The results of TARE not only take various factors and their interaction into consideration but also consider the time and geographic location of events. Extensive experiments on three location-based social network datasets show that TARE achieves better performance in recommendation accuracy than existing state-of-the-art methods.