计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 43-49.doi: 10.11896/jsjkx.210400130
所属专题: 智能数据治理技术与系统
王营丽1, 姜聪聪1, 冯小年2, 钱铁云1
WANG Ying-li1, JIANG Cong-cong1, FENG Xiao-nian2, QIAN Tie-yun1
摘要: 在基于位置的社交网络(Location-based Social Networks,LBSN)中,用户共享位置和与位置信息相关的内容。兴趣点推荐是LBSN的重要应用,根据用户历史访问签到记录推荐其可能感兴趣的位置。与其他推荐问题(如产品推荐或电影推荐)相比,用户对兴趣点的偏好在时间感知特征上尤为凸显。文中探索了时间感知特征对兴趣点推荐任务的影响,提出了时间感知的兴趣点推荐方法TAPR(Time Aware POI Recommendation)。该算法基于不同的时间尺度构建不同的关系矩阵,并且利用张量分解将构建出的多个关系矩阵分解从而得到用户与兴趣点的表示。最后,该算法利用余弦相似性计算用户与未访问POIs的相似性得分,并结合用户偏好建模的算法得到最终推荐分数。在两个公开数据集上的实验结果表明,TAPR算法比其他基于兴趣点推荐算法表现更好。
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[1]GAO H,TANG J,HU X,et al.Exploring temporal effects for location recommendation on location based social networks[C]//Proceedings of the 7th ACM Conference on Recommender Systems.ACM,2013:93-100. [2]YUAN Q,CONG G,MA Z,et al.Time-aware point-of-interest recommendation[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2013:363-372. [3]MUHAMMAD A S,ROHIT K,TOON C,et al.Location in-fluence in location-based social networks[C]//Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.2017:621-630. [4]CHO E,MYERS S A,LESKOVEC J.Friendship and mobility:user movement in location-based social networks[C]//Procee-dings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2011:1082-1090. [5]ZHAO S,LYU M R,KING I.Aggregated Temporal TensorFactorization Model for Point-of-interest Recommendation[C]//Neural Information Processing-23rd International Conference ICONIP.2016:450-458. [6]NICKEL M,TRESP V,KRIEGEL H P.A Three-Way Modelfor Collective Learning on Multi-Relational Data[C]//International Conference on Machine Learning.Omnipress,2011. [7]YE M,YIN P,LEE W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2011:325-334. [8]CHENG C,YANG H,KING I,et al.(2012) Fused matrix factorization with geographical and social influence in location-based social networks[C]//Proceedings of the Twenty-sixth AAAI Conference on Artificial Intelligence.2012:17-23. [9]MA H,ZHOU D,LIU C,et al.Recommender systems with social regularization[C]//Proceedings of the Fourth ACM International Conference on Web Search and Data Mining.ACM,2011:287-296. [10]CHENG C,YANG H,LYU M R,et al.Where you like to go next:successive point-of-interest recommendation [C]//Proceedings of the Twenty-third International Joint Conference on Artificial Intelligence.AAAI Press,2013:2605-2611. [11]MA L B,QIN X J.Topic-Location-Category Aware Point-of-interest Recommendation[J].Computer Science,2020,47(9):81-87. [12]RENDLE S,FREUDENTHALER C,SCHMIDT-THIEME L.Factorizing personalized Markov chains for next-basket recommendation[C]//Proceedings of the 19th International Confe-rence on World Wide Web.ACM,2010:811-820. [13]HAN P,LI Z,LIU Y,et al.Contextualized Point-of-InterestRecommendation[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence,IJCAI.2020:2484-2490. [14]CHEN J,MENG X W,JI W Y,et al.POI RecommendationBased on Multidimensional Context-aware Graph Embedding Model[J].Journal of Software,2020,31(12):3700-3715. [15]LIM N,HOOI B,NG S K,et al.STP-UDGAT:Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation[C]//The 29th ACM Internatio-nal Conference on Information and Knowledge Management.CIKM,2020:845-854. [16]FENG S,TRAN V T,CONG G,et al.HME:A Hyperbolic Metric Embedding Approach for Next-POI Recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.2020:1429-1438. [17]LI X,GAO C,LI X L,et al.Rank-geofm:A ranking based geographical factorization method for point of interest recommendation[C]//Proceedings of the 38rd International ACM SIGIR Conference on Rresearch and Development in Information Retrieval.2015:433-442. [18]ZHANG J D,CHOW C Y,LORE Y L.Exploiting sequential influence for location recommendations[C]//Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.2014:103-112. [19]LIAN D,ZHAO C,XIE X,et al.Geomf:joint geographical mo-deling and matrix factorization for point-of-interest recommendation[C]//The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2014:831-840. [20]LIU Y,PHAM T A N,CONG G,et al.An experimental evaluation of point-of-interest recommendation in location-based social networks[C]//Proc.VLDB Endow,2017. [21]WANG H,SHEN H,OUYANG W,et al.Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence(IJCAI 2018).2018:3877-3883. |
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