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.
Corresponding Authors:
LI Sheng, Ph.D., associate professor. His research interests include image processing, compressive sensing and recommendation system
About author:: DONG Chanjuan, master student. Her research interests include data mining and re-commendation system. HE Xiongxiong, Ph.D., professor. His research interests include signal processing, medical image processing and recommendation system. MA Yue, master student. Her research interests include image processing and reco-mmendation system.
DONG Chanjuan,LI Sheng,HE Xiongxiong等. Point of Interest Recommendation Algorithm Integrating Geo-Category Information and Implicit Social Relationship[J]. , 2021, 34(2): 106-116.
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2021, Vol. 34 
