Next Location Recommendation with Bi-Directional Inference and Similarity-Aware Enhancement

The greatest challenge of location recommendation is data sparsity. Some works introduce auxiliary information such as social links and semantics to enrich the representation. However, there are still two main limitations. Firstly, existing models only investigate how a person chooses POIs (Point-of-Interests), highlighting the user interest in locations from a human perspective, limited by users’ sparse trajectory. Secondly, they recommend POIs by one’s trajectory independently. However, it is hard to uncover specific behavior patterns or daily routines individually for each user. To address these problems, we develop a novel method for the next location recommendation via bi-directional inference and similarity-aware enhancement (SimBIN). Specifically, it considers the location recommendation task from both the human and location view, conducting the next POI inference and the next visitor inference synchronously. In particular, we employ the adaptive learning strategy to refine the model based on the conformity of two inference parts. Moreover, for the POI inference, we take the highly similar users to approximate a user’s behavior for enriching the next POI candidates. It is similar for the user inference. Experiments show that the proposed SimBIN significantly outperforms the state-of-the-art approaches. Furthermore, each key component can be refined and generally used in other frameworks. By establishing a bidirectional probabilistic collaboration framework, this study not only advances the theoretical understanding of mutual regularization in spatial-temporal modeling, but also provides a possible solution for personalized location-aware services and urban trajectory planning.