Recommending irregular regions using graph attentive networks

Abstract

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.We emphasize two facts in this paper: (1) there simultaneously exists ROI-level and POI-level implicitness that blurs the users’ underlying preferences; and (2) individual POIs should have non-uniform weights and more importantly, the weights should vary across different users. To address these issues, we contribute a novel solution, namely GANR2 (Graph Attentive Neural Network for Region Recommendation). Specifically, to learn the user preferences over irregular ROIs, we provide a principled neural network equipped with two attention modules: the POI-level attention module, to select the informative POIs of one ROI, and the ROI-level attention module, to learn the ROI preferences. Moreover, we learn the interactions between users and ROIs under the NGCF (Neural Graph Collaborative Filtering) framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.