基于位置社交网络的上下文感知的兴趣点推荐
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摘要
随着基于位置社交网络(Location-Based Social Networks,LBSN)的快速发展,兴趣点(Point-of-Interest,POI)推荐为基于位置的服务提供了前所未有的机会.兴趣点推荐是一种基于上下文信息的位置感知的个性化推荐.然而用户-兴趣点矩阵的极端稀疏给兴趣点推荐的研究带来严峻挑战.为处理数据稀疏问题,文中利用兴趣点的地理、文本、社会、分类与流行度信息,并将这些因素进行有效地融合,提出一种上下文感知的概率矩阵分解兴趣点推荐算法,称为TGSC-PMF.首先利用潜在狄利克雷分配(Latent Dirichlet Allocation,LDA)模型挖掘兴趣点相关的文本信息学习用户的兴趣话题生成兴趣相关分数;其次提出一种自适应带宽核评估方法构建地理相关性生成地理相关分数;然后通过用户社会关系的幂律分布构建社会相关性生成社会相关分数;另外结合用户的分类偏好与兴趣点的流行度构建分类相关性生成分类相关分数,最后利用概率矩阵分解模型(Probabilistic Matrix Factorization,PMF),将兴趣、地理、社会、分类的相关分数进行有效地融合,从而生成推荐列表推荐给用户感兴趣的兴趣点.该文在一个真实LBSN签到数据集上进行实验,结果表明该算法相比其他先进的兴趣点推荐算法具有更好的推荐效果.
The rapid development of location-based social networks(LBSNs)has provided an unprecedented opportunity for better location-based services through Point-of-Interest(POI)recommendation.POI recommendation is a personalized,location-aware,and context depended recommendation.However,extreme sparsity of user-POI matrix creates a severe challenge.In this paper,we propose a context-aware probabilistic matrix factorization method called TGSCPMF for POI recommendation,exploiting geographical information,text information,social information,categorical information and popularity information,incorporating these factors effectively.First,we exploit an aggregated Latent Dirichlet Allocation(LDA)model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score.Second,we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score.Third,we build social relevance through the power-law distribution of user social relations to generate social relevance score.Then,we model the categorical correlations which combine the category bias of users and the popularity of POIs into categorical relevance score.Further,weexploit probabilistic matrix factorization model(PMF)to integrate the interest,geographical,social and categorical relevance scores for POI recommendation.Finally,we implement experiments on a real LBSN check-in dataset.Experimental results show that TGSC-PMF achieves significantly superior recommendation quality compare to other state-of-the-art POI recommendation techniques.
The rapid development of location-based social networks(LBSNs)has provided an unprecedented opportunity for better location-based services through Point-of-Interest(POI)recommendation.POI recommendation is a personalized,location-aware,and context depended recommendation.However,extreme sparsity of user-POI matrix creates a severe challenge.In this paper,we propose a context-aware probabilistic matrix factorization method called TGSCPMF for POI recommendation,exploiting geographical information,text information,social information,categorical information and popularity information,incorporating these factors effectively.First,we exploit an aggregated Latent Dirichlet Allocation(LDA)model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score.Second,we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score.Third,we build social relevance through the power-law distribution of user social relations to generate social relevance score.Then,we model the categorical correlations which combine the category bias of users and the popularity of POIs into categorical relevance score.Further,weexploit probabilistic matrix factorization model(PMF)to integrate the interest,geographical,social and categorical relevance scores for POI recommendation.Finally,we implement experiments on a real LBSN check-in dataset.Experimental results show that TGSC-PMF achieves significantly superior recommendation quality compare to other state-of-the-art POI recommendation techniques.
引文
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[21]Ference G,Ye M,Lee W C.Location recommendation for outof-town users in location-based social networks//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management.San Francisco,USA,2013:721-726
[22]Wang H,Terrovitis M,Mamoulis N.Location recommendation in location-based social networks using user check-in data//Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.Orlando,USA,2013:374-383
[23]Liu B,Xiong H.Point-of-interest recommendation in location based social networks with topic and location awareness//Proceedings of the SIAM International Conference on Data Mining.Austin,USA,2013:396-404
[24]Hu L K,Sun A X,Liu Y.Your neighbors affect your ratings:On geographical neighborhood influence to rating prediction//Proceedings of the 37th International ACM SIGIRConference on Research and Development in Information Retrieval.Gold Coast,Australia,2014:345-354
[25]Cheng C,Yang H Q,King I,Lyu M R.Fused matrix factorization with geographical and social influence in locationbased social networks//Proceedings of the 26th AAAIConference on Artificial Intelligence.Toronto,Canada,2012:17-23
[26]Kurashima T,Iwata T,Hoshide T,et al.Geo topic model:Joint modeling of user’s activity area and interests for location recommendation//Proceedings of the 6th ACMInternational Conference on Web Search and Data Mining.Rome,Italy,2013:375-384
[27]Liu X,Liu Y,Aberer K,Miao C Y.Personalized point-ofinterest recommendation by mining users’preference transition//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management.Burlingame,USA,2013:733-738
[28]Yao Z,Liu B,Fu Y,et al.User preferencelearning with multiple information fusion for restaurantrecommendation//Proceedings of the 2014SIAM International Conference on Data Mining.Philadelphia,USA,2014:470-478
[29]Yuan Q,Cong G,Ma Z Y,et al.Time-aware point-of-interest recommendation//Proceedings of the 36th ACM SIGIRConference on Research and Development in Information Retrieval.Dublin,Ireland,2013:363-372
[30]Yuan Q,Cong G,Sun A X.Graph-based point-of-interest recommendation with geographical and temporal influences//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management.Shanghai,China,2014:659-668
[31]Zhang J D,Chow C Y.iGSLR:Personalized geo-social location recommendation-A kernel density estimation approach//Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.Orlando,USA,2013:334-343
[32]Zhang J D,Chow C Y.CoRe:Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations.Journal of Information Sciences,2015,293(1):163-181
[33]Zhang J D,Chow C Y,Li Y H.Lore:Exploiting sequential influence for location recommendations//Proceedings of the22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.Dallas,USA,2014:103-112
[34]Ying J J C,Kuo W N,Tseng V S,Lu E H C.Mining user check-in behavior with a random walk for urban point-ofinterest recommendations.ACM Transactions on Intelligent Systems and Technology,2014,5(3):40.1-40.26
[35]Yang D Q,Zhang D Q,Yu Z Y,Wang Z.A sentiment enhanced personalized location recommendation system//Proceedings of the 24th ACM Conference on Hypertext and Social Media.Paris,France,2013:119-128
[36]Liu X,Wu W.Learning context-aware latent representations for context-aware collaborative filtering//Proceedings of the38th International ACM SIGIR Conference on Research and Development in Information Retrieval.Santiago,Chile,2015:887-890
[37]Rahimi S M,Wang X.Location recommendation based on periodicity of human activities and location categories//Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining.Gold Coast,Australia,2013:377-389
[38]Zhao Y L,Nie L Q,Wang X Y,Chua T S.Personalized recommendations of locally interesting venues to tourists via cross-region community matching.ACM Transactions on Intelligent Systems and Technology,2014,5(3):50.1-50.26
[39]Blei D M,Ng A Y,Jordan M I.Latent dirichlet allocation.Journal of Machine Learning Research,2003,3:993-1022
[40]Griffiths T L,Steyvers M.Finding scientific topics.Proceedings of the National Academy of Sciences of the United States of America,2004,101(Supplement 1):5228-5235
[41]Cheng Z Y,Caverlee J,Lee K,Sui D Z.Exploring millions of footprints in location sharing services//Proceedings of the5th International AAAI Conference on Weblogs and Social Media.Barcelona,Spain,2011:81-88
[42]Salakhutdinov R,Mnih A.Probabilistic matrix factorization//Proceedings of the Advances in Neural Information Processing Systems.Vancouver,Canada,2007:1257-1264
[2]Ye M,Yin P F,Lee W C,Lee D L.Exploiting geographical influence for collaborative point-of-interest recommendation//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.Beijing,China,2011:325-334
[3]Gao H J,Liu H.Mobile Social Networking:Data Analysis on Location-Based Social Networks.New York,USA:Springer,2014
[4]Gao H J,Tang J L,Hu X,Liu H.Content-aware point of interest recommendation on location-based social networks//Proceedings of the 29th AAAI Conference on Artificial Intelligence.Astin,USA,2015:1721-1727
[5]Li X T,Cong G,Li X L,et al.Rank-geofm:A ranking based geographical factorization method for point of interest recommendation//Proceedings of the 38th International ACMSIGIR Conference on Research and Development in Information Retrieval.Santiago,Chile,2015:433-442
[6]Levandoski J J,Sarwat M,Eldawy A,Mokbel M F.LARS:A location-aware recommender system//Proceedings of the28th IEEE International Conference on Data Engineering.Washington,USA,2012:450-461
[7]Liu B,Fu Y J,Yao Z J,Xiong H.Learning geographical preferences for point-of-interest recommendation//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Chicago,USA,2013:1043-1051
[8]Lian D F,Zhao C,Xie X,et al.GeoMF:Joint geographical modeling and matrix factorization for point-of-interest recommendation//Proceedings of the 20th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining.New York,USA,2014:831-840
[9]Cao Jiu-Xin,Dong Yi,Yang Peng-Wei,et al.POI recommendation based on meta-path in LBSN.Chinese Journal of Computers,2016,39(4):675-684(in Chinese)(曹玖新,董羿,杨鹏伟等.LBSN中基于元路径的兴趣点推荐.计算机学报,2016,39(4):675-684)
[10]Liu Y,Wei W,Sun A X,Miao C Y.Exploiting geographical neighborhood characteristics for location recommendation//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management.Shanghai,China,2014:739-748
[11]Hu B,Ester M.Spatial topic modeling in online social media for location recommendation//Proceedings of the 7th ACMConference on Recommender Systems.Hong Kong,China,2013:25-32
[12]Yin H Z,Cui B,Sun Y Z,et al.LCARS:A spatial item recommender system.ACM Transactions on Information Systems,2014,32(3):11.1-11.37
[13]Yin H Z,Sun Y Z,Cui B,et al.LCARS:A location-contentaware recommender system//Proceedings of the 19th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining.Chicago,USA,2013:221-229
[14]Farrahi K,Gatica-Perez D.Discovering routines from largescale human locations using probabilistic topic models.ACMTransactions on Intelligent Systems and Technology,2011,2(1):3.1-3.27
[15]Ye M,Shou D,Lee W C,et al.On the semantic annotation of places in location-based social networks//Proceedings of the17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Diego,USA,2011:520-528
[16]Yin Z J,Cao L L,Han J W,Huang T.Geographical topic discovery and comparison//Proceedings of the 20th International Conference on World Wide Web.Hyderabad,India,2011:247-256
[17]Ferrari L,Rosi A,Mamei M,Zambonelli F.Extracting urban patterns from location-based social networks//Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks.Chicago,USA,2011:9-16
[18]Agarwal D,Chen B C.Flda:Matrix factorization through latent dirichlet allocation//Proceedings of the 3rd ACMInternational Conference on Web Search and Data Mining.Hong Kong,China,2010:91-100
[19]Pennacchiotti M,Gurumurthy S.Investigating topic models for social media user recommendation//Proceedings of the 20th International Conference on World Wide Web.Hyderabad,India,2011:101-102
[20]Bao J,Zheng Y,Mokbel M F.Location-based and preferenceaware recommendation using sparse geo-social networking data//Proceedings of the 20th International Conference on Advances in Geographic Information Systems.Redondo Beach,California,USA,2012:199-208
[21]Ference G,Ye M,Lee W C.Location recommendation for outof-town users in location-based social networks//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management.San Francisco,USA,2013:721-726
[22]Wang H,Terrovitis M,Mamoulis N.Location recommendation in location-based social networks using user check-in data//Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.Orlando,USA,2013:374-383
[23]Liu B,Xiong H.Point-of-interest recommendation in location based social networks with topic and location awareness//Proceedings of the SIAM International Conference on Data Mining.Austin,USA,2013:396-404
[24]Hu L K,Sun A X,Liu Y.Your neighbors affect your ratings:On geographical neighborhood influence to rating prediction//Proceedings of the 37th International ACM SIGIRConference on Research and Development in Information Retrieval.Gold Coast,Australia,2014:345-354
[25]Cheng C,Yang H Q,King I,Lyu M R.Fused matrix factorization with geographical and social influence in locationbased social networks//Proceedings of the 26th AAAIConference on Artificial Intelligence.Toronto,Canada,2012:17-23
[26]Kurashima T,Iwata T,Hoshide T,et al.Geo topic model:Joint modeling of user’s activity area and interests for location recommendation//Proceedings of the 6th ACMInternational Conference on Web Search and Data Mining.Rome,Italy,2013:375-384
[27]Liu X,Liu Y,Aberer K,Miao C Y.Personalized point-ofinterest recommendation by mining users’preference transition//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management.Burlingame,USA,2013:733-738
[28]Yao Z,Liu B,Fu Y,et al.User preferencelearning with multiple information fusion for restaurantrecommendation//Proceedings of the 2014SIAM International Conference on Data Mining.Philadelphia,USA,2014:470-478
[29]Yuan Q,Cong G,Ma Z Y,et al.Time-aware point-of-interest recommendation//Proceedings of the 36th ACM SIGIRConference on Research and Development in Information Retrieval.Dublin,Ireland,2013:363-372
[30]Yuan Q,Cong G,Sun A X.Graph-based point-of-interest recommendation with geographical and temporal influences//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management.Shanghai,China,2014:659-668
[31]Zhang J D,Chow C Y.iGSLR:Personalized geo-social location recommendation-A kernel density estimation approach//Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.Orlando,USA,2013:334-343
[32]Zhang J D,Chow C Y.CoRe:Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations.Journal of Information Sciences,2015,293(1):163-181
[33]Zhang J D,Chow C Y,Li Y H.Lore:Exploiting sequential influence for location recommendations//Proceedings of the22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.Dallas,USA,2014:103-112
[34]Ying J J C,Kuo W N,Tseng V S,Lu E H C.Mining user check-in behavior with a random walk for urban point-ofinterest recommendations.ACM Transactions on Intelligent Systems and Technology,2014,5(3):40.1-40.26
[35]Yang D Q,Zhang D Q,Yu Z Y,Wang Z.A sentiment enhanced personalized location recommendation system//Proceedings of the 24th ACM Conference on Hypertext and Social Media.Paris,France,2013:119-128
[36]Liu X,Wu W.Learning context-aware latent representations for context-aware collaborative filtering//Proceedings of the38th International ACM SIGIR Conference on Research and Development in Information Retrieval.Santiago,Chile,2015:887-890
[37]Rahimi S M,Wang X.Location recommendation based on periodicity of human activities and location categories//Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining.Gold Coast,Australia,2013:377-389
[38]Zhao Y L,Nie L Q,Wang X Y,Chua T S.Personalized recommendations of locally interesting venues to tourists via cross-region community matching.ACM Transactions on Intelligent Systems and Technology,2014,5(3):50.1-50.26
[39]Blei D M,Ng A Y,Jordan M I.Latent dirichlet allocation.Journal of Machine Learning Research,2003,3:993-1022
[40]Griffiths T L,Steyvers M.Finding scientific topics.Proceedings of the National Academy of Sciences of the United States of America,2004,101(Supplement 1):5228-5235
[41]Cheng Z Y,Caverlee J,Lee K,Sui D Z.Exploring millions of footprints in location sharing services//Proceedings of the5th International AAAI Conference on Weblogs and Social Media.Barcelona,Spain,2011:81-88
[42]Salakhutdinov R,Mnih A.Probabilistic matrix factorization//Proceedings of the Advances in Neural Information Processing Systems.Vancouver,Canada,2007:1257-1264