关键词: 位置社交网络, 兴趣点推荐, 社交关系, 局部地理因素, 加权矩阵分解

Abstract:

POI recommendation is an important personalized service in Location-Based Social Network（LBSN）, which can help users discover POIs and improve the quality of information services. Aiming at the problem of data sparsity in POI recommendation, this paper proposes a POI recommendation algorithm combining social relationship and local geographic factors. This algorithm measures user similarity based on the common check-in and distance relationships among users in the social relationship, and builds a social model through user collaborative filtering. A local activity area for each user is divided, and the sign-in correlation is analyzed to establish a local geographic factor model. Based on weighted matrix decomposition, users’own preferences are mined, and social relationships and local geographic factors for POI recommendation are integrated. Experiments on the Gowalla dataset show that the proposed POI recommendation algorithm has higher accuracy and recall rate than other methods, which can effectively alleviate the problem of data sparsity and improve recommendation performance.

Key words: Location-Based Social Networks（LBSN）, Point-Of-Interest（POI） recommendation, social relationship, local geographic factors, weighted matrix factorization