Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (5): 173-178.DOI: 10.3778/j.issn.1002-8331.1811-0290

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POI Recommendation Algorithm with Fusing Social Relation and Geographical Information

ZHANG Jin, SUN Fuzhen, WANG Shaoqing, WANG Shuai, LU Xiangzhi   

  1. College of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong 255049, China
  • Online:2020-03-01 Published:2020-03-06



  1. 山东理工大学 计算机科学与技术学院,山东 淄博 255049


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



关键词: 兴趣点推荐, 矩阵分解, 社交信任, BPR