Multi-factor Fusion POI Recommendation Model

In the context of the rapid development of location-based socialnetworks (LBSN), point of interest (POI) recommendation becomes an importantservice in LBSN. The POI recommendation service aims to recommendsome new places that may be of interest to users, help users to better understandtheir cities, and improve users’ experience of the platform. Although the geographicinfluence, similarity of POIs, and user check-ins information have beenused in the existing work recommended by POI, little existing work consideredcombing the aforementioned information. In this paper, we propose to makerecommendations by combing user ratings with the above information. Wemodel four types of information under a unified POI recommendation frameworkand this model is called extended user preference model based on matrixfactorization, referred to as UPEMF. Experiments were conducted on two realworld datasets, and the results show that the proposed method improves theaccuracy of POI recommendations compared to other recent methods.