关键词: 兴趣点, 协同过滤, 社交信息, 地理信息, 相似度

Abstract:

The existing Point-of-Interest（POI） recommendation model based on user-based collaborative filtering considers two users have the same impact on each other. At the same time, only the user’s friend set is considered when calculating the similarity of social users, and the geographic information of user’s residence is not considered. In view of the above problems, this paper proposes a point of interest recommendation model which is Fuse User, Social and Geographical（FUSG）. Firstly, the asymmetric user impact and PageRank algorithm are integrated into the user-based collaborative filtering algorithm to mine the impact of user preferences on the POI recommendation system. Secondly, the similarity between users is calculated by combining the living distances and the common friends of social users, and the geographic features of user check-in are mined by using geographic information. Finally, the improved collaborative filtering algorithm, social information and geographic information are merged into FUSG model to recommend point-of-interest. The experimental results on real data sets show that the FUSG model not only alleviates the cold start problem, but also has higher recommendation results than other models.

Key words: points of interest, collaborative filtering, social information, geographic information, similarity