IMMPOI: An Interest-Aware Multimodal Adaptive Fusion Framework for POI Recommendations

The objective of the Point-of-Interest (POI) recommendation system is to predict potential future visits based on users' check-in histories. However, due to factors such as users' tendency to visit nearby locations, time and space mobility costs, users are more likely to visit a limited number of POIs within a confined geographical area. Consequently, POI recommendation faces a more severe data sparsity issue compared to other recommendation scenarios. Current research has shown that incorporating multimodal content information into POI recommendations can effectively alleviate the data sparsity problem. However, existing methods still have the following limitations: 1) Multimodal noise hinders the effective extraction of multimodal content. 2) Multimodal feature fusion ignores the differential impact of various modalities on user decisions. To address these issues, we propose anInterest-awareMultiModal adaptive fusion framework forPOIrecommendations (IMMPOI). Specifically, we propose an interest-oriented purifier to perform multimodal noise filtering based on user preference, and introduce a disentangled multimodal graph encoder to accurately capture fine-grained behavior features, multimodal features, geographical, and sequential relationships between users and POIs. Then, we develop an interest-aware multimodal fuser that learns comprehensive multimodal representations of users and POIs by adaptively integrating multimodal content features and context features based on a self-supervised strategy. Extensive experiments on four real-world datasets demonstrate that IMMPOI achieves a 6% to 10% performance improvement compared to state-of-the-art methods.