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.
[1] GUO L, WEN Y F, LIU F G. Location Perspective-Based Neighborhood-Aware POI Recommendation in Location-Based Social Networks. Soft Computing, 2019, 23(22): 11935-11945. [2] ZHANG J D, CHOW C Y. iGSLR: Personalized Geo-Social Location Recommendation: A Kernel Density Estimation Approach // Proc of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, USA: ACM, 2013: 334-343. [3] GAO R, LI J, DU B, et al. Exploiting Geo-Social Correlations to Improve Pairwise Ranking for Point-of-Interest Recommendation. China Communications, 2018, 15(7): 180-201. [4] LI H Y, GE Y, HONG R C, et al. Point-of-Interest Recommendations: Learning Potential Check-Ins from Friends // Proc of the 22nd ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining. New York, USA: ACM, 2016: 975-984. [5] REN Y Q, WANG Z, SUN X N, et al. A Multi-element Hybrid Location Recommendation Algorithm for Location Based Social Networks. IEEE Access, 2019, 7: 100416-100427. [6] PAN Z G, CUI L, WU X Y, et al. Deep Potential Geo-Social Relationship Mining for Point-of-Interest Recommendation. IEEE Access, 2019, 7: 99496-99507. [7] HU Y F, KOREN Y, VOLINSKY C. Collaborative Filtering for Implicit Feedback Datasets // Proc of the 8th IEEE International Conference on Data Mining. Washington, USA: IEEE, 2008: 263-272. [8] KUMAR R, VERMA B K, RASTOGI S S, et al. Social Popularity Based SVD++ Recommender System. International Journal of Computer Applications, 2014, 87(14): 33-37. [9] GAO R, LI J, LI X F, et al. A Personalized Point-of-Interest Re-commendation Model via Fusion of Geo-Social Information. Neurocomputing, 2018, 273: 159-170. [10] DU R Z, LU J J, CAI H Y. Double Regularization Matrix Factorization Recommendation Algorithm. IEEE Access, 2019, 7: 139668-139677. [11] SI Y L, ZHANG F Z, LIU W Y. An Adaptive Point-of-Interest Recommendation Method for Location-Based Social Networks Based on User Activity and Spatial Features. Knowledge-Based Systems, 2019, 163: 267-282. [12] LIU X, LIU Y, ABERER K, et al. Personalized Point-of-Interest Recommendation by Mining Users' Preference Transition // Proc of the 22nd ACM International Conference on Information and Know-ledge Management. New York, USA: ACM, 2013: 733-738. [13] HE J, LI X, LIAO L J. Category-Aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking // Proc of the 26th International Joint Conference on Artificial Intelligence. New York, USA: ACM, 2017: 1837-1843. [14] CHEN J L, LI X, CHEUNG W K, et al. Effective Successive POI Recommendation Inferred with Individual Behavior and Group Pre-ference. Neurocomputing, 2016, 210: 174-184. [15] DIVYAA L R, PERVIN N. Towards Generating Scalable Persona-lized Recommendations: Integrating Social Trust, Social Bias, and Geo-Spatial Clustering. Decision Support Systems, 2019, 122. DOI: 10.1016/j.dss.2019.05.006. [16] SI Y L, ZHANG F Z, LIU W Y. CTF-ARA: An Adaptive Method for POI Recommendation Based on Check-in and Temporal Features. Knowledge Based Systems, 2017, 128: 59-70. [17] 孟祥福,张霄雁,唐延欢,等.基于地理-社会关系的多样性与个性化兴趣点推荐.计算机学报, 2019, 42(11): 2574-2590. (MENG X F, ZHANG X Y, TANG Y H, et al. A Diversified and Personalized Recommendation Approach Based on Geo-Social Relationships. Chinese Journal of Computers, 2019, 42(11): 2574-2590.) [18] 王瑞琴,潘 俊,冯建军.基于信任计算和矩阵分解的推荐算法.模式识别与人工智能, 2018, 31(9): 786-796. (WANG R Q, PAN J, FENG J J. Recommendation Algorithm Based on Trust Computation and Matrix Factorization. Pattern Re-cognition and Artificial Intelligence, 2018, 31(9): 786-796.) [19] MA H, YANG H X, LYU M R, et al. SoRec: Social Recommendation Using Probabilistic Matrix Factorization // Proc of the 17th ACM Conference on Information and Knowledge Management. New York, USA: ACM, 2008: 931-940. [20] MA H, ZHOU D Y, LIU C, et al. Recommender Systems with Social Regularization // Proc of the 4th ACM International Confe-rence on Web Search and Data Mining. New York, USA: ACM, 2011: 287-296. [21] YANG B, LEI Y, LIU D Y, et al. Social Collaborative Filtering by Trust // Proc of the 23rd International Joint Conference on Artificial Intelligence. New York, USA: ACM, 2013: 2747-2753. [22] YANG B, LEI Y, LIU J M, et al. Social Collaborative Filtering by Trust. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1633-1647. [23] LIU B, FU Y J, YAO Z J, et al. Learning Geographical Prefe-rences for Point-of-Interest Recommendation // Proc of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2013: 1043-1051. [24] YE M, YIN P F, LEE W C, et al. Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation // Proc of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2011: 325-334. [25] CHENG C, YANG H Q, KING I, et al. Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks // Proc of the 26th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2012: 17-23. [26] LIAN D F, ZHAO C, XIE X, et al. GeoMF: Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation // Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2014: 831-840. [27] GUO G B, ZHANG J, YORKE-SMITH N. TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings // Proc of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2015: 123-129. [28] PHAM H, SHAHABI C, LIU Y. EBM: An Entropy-Based Model to Infer Social Strength from Spatiotemporal Data // Proc of the ACM SIGMOD International Conference on Management of Data. New York, USA: ACM, 2013: 265-276. [29] TOBLER W R. A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 1970, 46(2): 234-240.
2021, Vol. 34 
