User Multi-behavior Enhanced POI Recommendation with Efficient and Informative Negative Sampling

Point-of-Interest (POI) recommendation plays a crucial role in the location-based social networks (LBSNs), while the extreme sparsity of user check-in data severely impedes the further improvement of POI recommendation. Existing works jointly analyse user check-in behaviors (i.e., positive samples) and POI distribution to tackle this issue. However, introducing user multi-modal behaviors (e.g., online map query behaviors), as a supplement of user preference, still has not been explored. Further, they also neglect to exploit why users don’t visit the POIs (i.e., negative samples). To these ends, in this paper, we propose a novel approach, user multi-behavior enhanced POI recommendation with efficient and informative negative sampling, to promote recommendation performance. In particular, we first extract three types of relationships, i.e., POI-query, user-query and POI-POI, from map query and check-in data. After that, a novel approach is proposed to learn user and POI representations in each behavior through these heterogeneous relationships. Moreover, we design a negative sampling method based on geographic information to generate efficient and informative negative samples. Extensive experiments conducted on real-world datasets demonstrate the superiority of our approach compared to state-of-the-art recommenders in terms of different metrics.