Recommending irregular regions using graph attentive networks

Due to the prevalence of human activity in urban spaces, recommending ROIs (region-of-interests) to users, especially irregular ROIs, becomes an important task in location-based social networks. A fundamental problem is how to aggregate users’ preferences over POIs (point-of-interests) to infer the users’ region-level mobility patterns. The majority of existing studies ignore the users’ implicit interactions with individual POIs when addressing this issue. For example, a user check-in a region cannot provide any specific information about how the user likes this region (we call this phenomenon “ROI-level” implicitness) and which POI in this region the user is interested in (i.e., “POI-level” implicitness). Furthermore, existing studies adopt predefined strategies for region-level preference aggregation, that is, initializing the importance of different POIs with identical weights, which is insufficient to model the reality of social networks.