User Type Mining and Multi-information Fusion based Cross-region POI Recommendation Method

With the emergence of location-based services and the proliferation of location-aware devices, a significant volume of location-based social media data, such as check-in data, has been generated. Consequently, this has led to the development of Point of Interest (POI) recommendation systems. However, most research has primarily focused on POI recommendations within a single region, with relatively little attention given to cross-region POI recommendations. In this study, a cross-region POI recommendation model named UTMFPR is proposed. Initially, within the user type feature extraction module, check-in features are extracted based on user check-in data. User type embeddings are then obtained using an attention network and a Sparse Autoencoder (SAE). Subsequently, within the multi-information fusion module, user home-town preferences are extracted based on check-in data and user interest drift are achieved through nonlinear mapping. The Neural Topic Model (NTM) is employed to uncover user travel intentions. A fusion mechanism is then designed to integrate user types, interest drift, and travel intentions, thereby deriving user target region preference. Finally, within the target region POI recommendation module, POI embeddings for target region are generated by integrating geographical information. These embeddings are subsequently combined with user target region preference to facilitate the recommendation process. Experiments were conducted on two real-world datasets, and the results indicate that UTMFPR surpasses existing methods in recommendation accuracy for cross-region POI recommendations.