计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 176-183.doi: 10.11896/jsjkx.201100021

• 大数据&数据科学 • 上一篇    下一篇

兴趣点推荐方法研究综述

邢长征, 朱金侠, 孟祥福, 齐雪月, 朱尧, 张峰, 杨一鸣   

  1. 辽宁工程技术大学电子与信息工程学院 辽宁 葫芦岛125105
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 孟祥福(marxi@126.com)
  • 作者简介:marxi@126.com
  • 基金资助:
    国家重点研发计划项目(2018YFB1402901);国家自然科学基金项目(61772249);辽宁省教育厅一般项目(LJ2019QL017)

Point-of-interest Recommendation:A Survey

XING Chang-zheng, ZHU Jin-xia, MENG Xiang-fu, QI Xue-yue, ZHU Yao, ZHANG Feng, YANG Yi-ming   

  1. School of Electronic and Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:XING Chang-zheng,born in 1967,professor,is a member of China Computer Federation.His main research interests include distributed database management system,data stream clustering and recommender systems.
    MENG Xiang-fu,born in 1981,Ph.D,professor,is a member of China Computer Federation.His main research interests include Web databases top-k query,spatial data management,recommender systems and data visualization.
  • Supported by:
    National Key Research and Development Program of China(2018YFB1402901),National Natural Science Foundation of China(61772249) and General Project of Liaoning Provincial Department of Education(LJ2019QL017).

摘要: 兴趣点(Point-Of-Interest,POI) 推荐是基于位置的社交网络(Location-Based Social Networks,LBSN)中一项重要的服务,无论对商家还是对客户都有重要的影响,并且兴趣点数据作为时空数据的典型更是得到了广泛关注,因此兴趣点推荐近年来已经成为学术界的热门研究课题。文章分析了兴趣点推荐的影响因素,对传统兴趣点推荐方法进行了总结,分析了最新的基于图嵌入方法以及图神经网络在兴趣点推荐领域中的应用,最后对兴趣点推荐所面临的挑战以及未来的研究趋势加以分析。

关键词: 图嵌入方法, 图神经网络, 兴趣点推荐, 影响因素

Abstract: Point-of-interest (POI) recommendation is an important service in location-based social networks (LBSN),which has an important impact on both merchants and customers.As a typical example of spatio-temporal data,POI recommendation has been widely concerned,so it has become a hot research topic in academic circles in recent years.This paper analyzes the influencing factors of interest point recommendation,summarizes the traditional methods of interest point recommendation,the latest graph-based embedding methods and the application of graph neural networks in the field of point-of-interest recommendation are analyzed.Finally,it analyzes the challenges faced by points of interest recommendation and future research trends.

Key words: Graph embedding method, Graph neural network, Influencing factors, Point-of-interest recommendation

中图分类号: 

  • TP301
[1]RESNICK P,VARIAN H R.Recommender systems[J].Communications of the ACM,1997,40(3):56-58.
[2]LONG Y,ZHAO P,SHENG V S,et al.Social PersonalizedRanking Embedding for Next POI Recommendation[C]//Proceedings of the International Conference on Web Intelligence.2017:91-105.
[3]HE J,LI X,LIAO L,et al.Inferring a personalized next point-of-interest recommendation model with latent behavior patterns[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2016:137-143.
[4]FENG S,LI X,ZENG Y,et al.Personalized ranking metric embedding for next new POI recommendation[C]//Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence.2015:2069-2075.
[5]CHENG C,YANG H,LYU M R,et al.Where you like to go next:successive point-of-interest recommendation[C]//Procee-ding of the International Joint Conference on Artificial Intelligence.2013:2605-2611.
[6]YU Z,XU H,YANG Z,et al.Personalized travel package with Multi-Point-of-Interest recommendation based on crowdsourced user footprints[C]//Proceeding of the IEEE Transactions on Human-Machine Systems.2016:151-158.
[7]LOU P,ZHAO G,QIAN X,et al.Schedule a Rich Sentimental Travel via Sentimental POI Mining and Recommendation[C]//Proceedings of IEEE Second International Conference on Multimedia Big Data.2016:33-40.
[8]ZHANG C,LIANG H,WANG K,et al.Personalized Trip Re-commendation with POI Availability and Uncertain Traveling Time[C]//Proceedings of the ACM International Conference on Information and Knowledge Management.2015:911-920.
[9]WANG W,YIN H,CHEN L,et al.Geo-SAGE:A Geographical Sparse Additive Generative Model for Spatial Item Recommendation[C]//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2015:1255-1264.
[10]LI H,HONG R,WU Z,et al.A Spatial-Temporal Probabilistic Matrix Factorization Model for Point-of-Interest Recommendation[C]//Proceedings of the Siam International Conference on Data Mining.Miami.2016:117-125.
[11]ZHANG J,CHOW C Y.GEO S C.Exploiting Geographical,Social and Categorical Correlations for Point-of-Interest Recommendations[C]//Procee-dings of the International ACM SIGIR Conference on Research and Development in Information Retrieval.2015:443-452.
[12]DING Y,LI X.Time weight collaborative ltering[C]//Proceedings of the ACM International Conference on Information and Knowledge Management.2005:485-492.
[13]ZHAO S,ZHAO T,KING I,et al.Geo-teaser:Geo-temporal sequential embedding rank for point-of-interest recommendation[C]//Proceedings of the International Conference on World Wide Web Companion.2017:153-162.
[14]GRIESNER J B,ABDESSALEM T,NAACKE H.Poi recommendation:towards fused matrix factorization with geographical and temporal influences[C]//Proceedings of the ACM Confe-rence on Recommender Systems.2015:301-304.
[15]GAO H,TANG J,HU X,et al.Exploring temporal effects for location recommendation on location-based social networks[C]//Proceedings of the ACM Conference on Recommender Systems.2013:93-100.
[16]BERJANI B,STRUFE T.A recommendation system for spots in location-based online social networks[C]//Proceedings of the Workshop on Social Network Systems.2011:1-6.
[17]GUO L,WEN Y,LIU F.Location perspective-based neighborhood-aware poi recommendation in location-based social networks.Soft Computing,2019(23):11935-11945.
[18]ZHAO S G,KING I W,MICHAEL R L.Capturing geographical influence in poi recommendations[C]//Proceedings of the International Conference on Neural Information Processing.2013:530-537.
[19]CHEN C C,LIU Z Q,ZHAO P L,et al.Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization[C]//Proceedings of the Association for the Advance of Artificial Intelligence AAAI.2018:257-264.
[20]JAMALI M,ESTER M.A matrix factorization technique with trust propagation for recommendation in social networks[C]//Proceedings of the fourth ACM Conference on Recommender Systems.2010:135-142.
[21]MA H,YANG H,LYU M,et al.Sorec:social Recommendation using probabilistic matrix factorization[C]//Proceedings of the ACM Conference on Information and Knowledge Management.2008:931-940.
[22]YE M,YIN P,LEE W C.Location recommendation for location-based social networks[C]// Proceedings of the SIGSPATIAL International Conference on Advances in Geographic Information Systems.2010:458-461.
[23]CHENG C,YANG H,KING I,et al.Fused matrix factorization with geographical and social influence in location-based social networks[C]//Proceedings of the 26th AAAI Conference on Artificial Intelligence.2012:48-48.
[24]HUA W,WANG Z,WANG H,et al.Short text understanding through lexical-semantic analysis[C]//Proceedings of the International Conference on Data Engineering.2015:495-506.
[25]WANG Z Y,CHENG J P,WANG H X,et al.Short text understanding:a survey[J].Journal of Computer Research andDeve-lopment,2016,53(2):262-269.
[26]GAO H J,TANG J L,HU X,et al.Content-aware point of interest recommendation on location-based social networks[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.2015:1721-1727.
[27]HU B,ESTER M.Social topic modeling for point-of-interestrecommendation in location-based social networks[C]//Proceedings of the IEEE International Conference on Data Mining.2014:845-850.
[28]LIAN D F,GE Y,ZHANG F Z,et al.Content-aware collaborative filtering for location recommendation based on human mobi-lity data[C]//Proceedings of the Industrial Conference on Data Mining.2015:261-270.
[29]SUN L,LUO B S,GAO R.Synthetic rank-oriented POI recommendation algorithm based on local collaborative ranking exploiting review information and geographical information[J].Computer Engineering and Applications,2018,35(10):2980-2986.
[30]YANG D Q,ZHANG D Q,YU Z Y,et al.A sentiment-en-hanced personalized location recommendation system[C]//Proceedings of the ACM Conference on Hypertext and Social Media.2013:119-128.
[31]LIAN D,ZHAO C,XIE X,et al.GeoMF:Joint geographicalmodeling and matrix factorization for point-of-interest recommendation[C]//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2014:831-840.
[32]ZHANG F,YUAN N J,ZHENG K,et al.Exploiting diningpreference for restaurant recommendation[C]//Proceeding of the International Conference on World Wide Web,International World Wide Web Conf.on Steering Committee.2016:725-735.
[33]LUO X,ZHOU M C,LI S,et al.An efficient second-order approach to factorizing sparse matrices in recommender systems[J].IEEE Transactions on Industrial Informatics,2015,11(4):946-956.
[34]LI X,JIANG M,HONG H,et al.A time-aware personalizedpoint-of-interest recommendation via high-order tensor factorization[J].Proceeding of the Trans on Information Systems (TOIS),2017,35(4):31.
[35]DAI L,MENG X W,ZHANG Y J,et al.Restaurant Recommendation Model with Multiple Information Fusion[J].Journal of Software,2019,30(9):2869-2885.
[36]LI Q,XU X H,LIU H X,et al.Time-aware point-of-interest recommendation based on dynamic heterogeneous network in LBSN[J].Computer Engineering and Applications,2020,56(11):67-74.
[37]TOBLER W R.A computer movie simulating urban growth in the Detroit region[J].Economic Geography,1970,46:234-240.
[38]FU Y,LIU B,GE Y,et al.User preference learning with multiple information fusion for restaurant recommendation[C]//Proceeding of the SIAM International Conference on Data Mining.2014:470-478.
[39]WANG H,TERROVITIS M,MAMOULIS N.Location recommendation in location-based social networks using user check-in data[C]//Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.2013:364-373.
[40]MENG X F,ZHANG X Y,TANG Y H,et al.A Diversified and Personalized Recommendation Approach Based on Geo-Social Relationships[J].Chinese Journal of Computers,2019:2575-2589.
[41]YI R X,SONG M N,SONG J D.Point-of-interest recommendation based on the user check-in behavior[J].Chinese Journal of Computers,2017,40(1):28-50.
[42]TANG J,QUM M,MEI Q.PTE:predictive text embeddingthrough large-scale heterogeneous text networks[C]//Procee-ding of the Special Interest Group on Spatial Information.2015:1165-1174.
[43]CHRISTOFORIDIS G,KEFALAS P,PAPADOPOULOS A,et al.Recommendation of Points-of-Interest using graph embeddings[C]//DSAA.2018:31-40.
[44]LI H,GE Y,HONG R,et al.Point-of-interest recommenda-tions:learning potential check-ins from friends[C]//Proceeding of the Special Interest Group on Spatial Information.2016:975-984.
[45]MANOTUMRUKSA J,MACDONALD C,OUNIS I.A personalised ranking framework with multiple sampling criteria for venue recommendation[C]//Proceedings of the Conference on Information and Knowledge Management.2017:1469-147.
[46]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]//Proceedings of the Uncertainty in Artificial Intelligence.2009:452-461.
[47]YUAN F,GUO G,JOSE J,et al.Joint geo-spatial preference and pairwise ranking for point-of-interest recommendation[C]//Proceedings of the IEEE International Conference on Integrated Circuits,Technologies and Applications.2016:46-53.
[48]CHENG C,YANG H.G,MICHAEL R.et al.Where you like to go next:Successive point-of-interest recommendation[C]//Proceeding of the International Joint Conference on Artificial Intelligence.2013:2605-2611.
[49]ZHANG J D,CHOW C Y,LI Y H.Exploiting sequential influence for location recommendations[C]//Proceeding of the Special Interest Group on Spatial Information.2014:103-112.
[50]ZHENG Y Z,ZHA Z J,CHUA T C.Mining travel patterns from geotagged photos[C]//Proceedings of the ACM Transactions on Intelligent Systems and Technology.2012:1-18.
[51]XIE M,YIN H Z,XU F J,et al.Graph-based metric embedding for next POI recommendation[C]//Proceedings of the Web Information Systems Engineering.2016:207-222.
[52]XIE M,YIN H Z,WANG H,et.al.Learning graph-based POI embedding for location-based recommendation[C]//Proceedings of the Conference on Information and Knowledge Management.2016:15-24.
[53]KAWAMOTO T,TSUBAKI M,OBUCHI T.Mean-field theory of graph neural networks in graph partitioning[C]//Advances in Neural Information Processing Systems.2018:4361-4371.
[54]WU S W,ZHANG Y X,GAO C L,et al.Anonymous recommen-dation of Point-of-Interest in mobile networks by graph convolution network[C]//Proceedings of the Data Science and Engineering.2020.
[55]LU Y S,HUANG J L.GLR:A graph-based latent representation model for successive POI recommendation[J].Future Generation Computer Systems,2019,102:230-244.
[56]LI L F,LIU Z,WEI G M,et al.Cross-Domain Recommendation AlgorithmBased on Sharing Knowledge Pattern[J].Acta Electronica Sinica,2018,46(8):1947-1953.
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