关键词: 兴趣点推荐, 矩阵分解, 社交信任, BPR

Abstract:

POI（Point-Of-Interest） recommendation for location-based social network exist some problems such as sparse user check-in matrix, low recommendation accuracy and inadequate use of context information. This paper proposes a matrix decomposition algorithm TGMF to solve the above problems. First, it uses BPR model to optimize the process of matrix decomposition and improves the generation strategy of partial order relations. Secondly, it fuses the trust effect and the similarity computation to improve the accuracy of recommendation. Finally, two models are combined to get the final preference list of users, and top-[k] interest points are recommended to users. Experimental results show that on real data sets Gowalla and Foursquare, the proposed algorithm is superior to the traditional interest point recommendation algorithm in precision and recall.

Key words: Point-Of-Interest（POI） recommendation, matrix factorization, social trust, BPR