Abstract:Aiming at the problems in the existing point of interest recommendation algorithms, such as check-in data sparsity, difficulties in obtaining social relation and lack of consideration of user individuality, a point of interest recommendation algorithm integrating geo-category information and implicit social relationship is proposed. Firstly, user check-in category information is considered, and user check-in location matrix and category matrix are decomposed simultaneously to reduce the impact of data sparsity. On the basis of explicit social relations, the method of information entropy is employed to measure user implicit social relations to alleviate the sparse problem of social networks, and then the user implicit social relations are added to the matrix factorization model by regularization method. Finally, the adaptive kernel density estimation method is adopted to personalize the impact of geographic information on user check-in behavior to improve the accuracy of recommendation. Experiments on Foursquare and Yelp datasets verify the effectiveness of the proposed algorithm.
董婵娟, 李胜, 何熊熊, 马悦. 融合地理信息、种类信息与隐式社交关系的兴趣点推荐算法[J]. 模式识别与人工智能, 2021, 34(2): 106-116.
DONG Chanjuan, LI Sheng, HE Xiongxiong, MA Yue. Point of Interest Recommendation Algorithm Integrating Geo-Category Information and Implicit Social Relationship. , 2021, 34(2): 106-116.
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