POI Recommendation Based on First-Order Collaborative Filtering Tree

Abstract

Point-Of-Interest (POI) recommendation plays an important role in Location-Based Social Networks(LBSN), which is widely used in popular attraction recommendations and travel route planning applications. The traditional recommendation algorithms fail to make full use of social relationships, user check-in distribution features and geographic information because they only use a simple linear function to model the above features. In order to solve the existing problems, we propose a recommendation framework-NCFT(Neural Collaborative Filtering Tree), which can fuse various side information. In the NCFT model, we propose an unsupervised user check-in distribution feature extractor, namely CD-Ex, which unsupervised learning user checkin distribution features. We also propose to build a user-based collaborative filtering tree and item-based collaborative filtering tree, and use the idea of messaging to learn deep representations of users and items. In these two modules, we use multi-head attention and vanilla attention to learn the representations of users and POIs. As for the user social relationship, we use the user's friends in the user-based collaborative tree to assign weights and aggregate his friends' features. The experimental results show that our model has a significant improvement in AUC and F1 compared to other models.