A novel POI recommendation method based on trust relationship and spatial-temporal factors

Existing point-of-interesting (POI) recommendation methods lack sufficient integration of information related to the features of individual users and their corresponding contexts. Personalized location-based services built upon these methods have a few limitations such as low recommendation accuracy and untapped potential interests of users. To overcome these limitations, we propose an efficient POI recommendation method based on multiple factors (i.e., preference, social relationship, and spatial-temporal factors) to improve the recommendation accuracy and alleviate the cold-start and sparsity problems. Unlike existing methods, the proposed method considers the features of each factor. First, we identify direct and indirect trust relationships, upon which the improved trust relationship measurement methods are built respectively. Second, we fuse the comprehensive trust relationship, user preference, check-in time, and geographical location into a matrix factorization model. We pay special attention to the attraction of items, correlation between items, trajectory composed of locations, and the influence of interest-forgetting during the fusing process. Finally, we generate recommendation lists of POIs for the target users. With three real-world datasets, experimental and analytical results show that the proposed method outperforms existing methods, while alleviating the cold-start and sparsity problems that commonly hinder POI recommender systems. Theoretically, our study contributes to the effective usage of multidimensional data science and analytics for POI recommender system design. In practice, our results can be used to improve the quality of personalized POI recommendation services for websites and applications.

Automated summary

The proposed POI recommendation method integrates user preferences, social relationships, and spatial-temporal factors to enhance accuracy and address common cold-start and sparsity issues. Outperforming existing methods, it contributes to improving the quality of personalized POI recommendation services.