Computer Science ›› 2020, Vol. 47 ›› Issue (9): 81-87.doi: 10.11896/jsjkx.191100120

• Database & Big Data & Data Science • Previous Articles     Next Articles

Topic-Location-Category Aware Point-of-interest Recommendation

MA Li-bo, QIN Xiao-lin   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Received:2019-11-15 Published:2020-09-10
  • About author:MA Li-bo,born in 1997,postgraduate.His main research interests include data management and recommendation system.
    QIN Xiao-lin,born in 1953,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include spatio-temporal database,distributed data management and security,etc.
  • Supported by:
    National Natural Science Foundation of China (61373015,61728204).

Abstract: With the continuous development of Location-Based Social Networks(LBSN),Point-of-Interest(POI) recommendations that help users explore new locations and merchants discover potential customers has received widespread attention.How-ever,due to the high sparsity of the users’ check-in data,POI recommendation faces serious challenges.To cope with this challenge,this paper explores the textual information,geographic information,and category information,incorporating interest topics,geographical influence,and category preference factors effectively,and proposes a topic-location-category aware collaborative filtering algorithm called TGC-CF for POI recommendation.The proposed algorithm uses the Latent Dirichlet Allocation(LDA) model to learn the interest topics distribution of users and calculate the similarity of interest topics distribution among users by mining textual information associated with POIs,models geographical influence by combining geographic distance and user’s regionalpre-ference,uses the TF-IDF statistical method to assess the target user’s preference for the category and consider the impact of other users’ category preference in the recommendation process,and finally integrate these influencing factors into a collaborative filtering recommendation model to generate a list of recommendations containing POIs that users are interested in.Experimental results on two real data sets show that TGC-CF algorithm performs better than other recommendation algorithms.

Key words: Location-based social networks, POI recommendation, Topic model, Geographical influence, Collaborative filtering

CLC Number: 

  • TP311
[1] SASSI I B,MELLOULI S,YAHIA S B.Context-aware recommender systems in mobile environment:On the road of future research[J].Information Systems,2017,72:27-61.
[2] WANG H,FU Y,WANG Q,et al.A location-sentiment-aware recommender system for both home-town and out-of-town users[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2017:1135-1143.
[3] TOBLER W R.A Computer Movie Simulating Urban Growth in the Detroit Region[J].Economic Geography,1970,46(Supp 1):234-240.
[4] STEPAN T,MORAWSKI J M,DICK S,et al.Incorporatingspatial,temporal,and social context in recommendations for location-based social networks[J].IEEE Trans.Comput Soc.Syst.,2016,3(4):164-175.
[5] WU H,SHAO J,YIN H,et al.Geographical Constraint andTemporal Similarity Modeling for Point-of-Interest Recommendation[C]//International Conference on Web Information Systems Engineering.2015:426-441.
[6] LIAN D,ZHAO C,XIE X,et al.Geomf:joint geographical modeling and matrix factorization for point-of-interest recommendation[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2014:831-840.
[7] XING S,LIU F,WANG Q,et al.Content-aware point-of-interest recommendation based on convolutional neural network[J].Appl.Intell.,2019,49:858.
[8] REN X Y,SONG M N,SONG J D.Context-aware Point-of-interest Recommendation in Location-Based Social Networks[J].Chinese Journal of Computers,2017,40(4):824-841.
[9] ZHU Z Q,CAO J X,WENG C H.Location-time-sociality aware personalized tourist attraction recommendation in LBSN[C]//IEEE 22nd International Conference on Computer Supported Cooperative Work in Design.2018:636-641.
[10] WANG H,TERROVITIS M,MAMOULIS N.Location recommendationin location-based social networks using user check-in data[C]//Proceedings of the 21st ACM SIGS PATIAL International Conference on Advances in Geographic Information Systems.2013:374-383.
[11] 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.2013:363-372.
[12] 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[J].Knowledge-Based Systems,2019,163:267-282.
[13] BAO J,ZHENG Y,MOKBEL M F.Location-based and prefe-rence-aware recommendation using sparse geo-social networking data[C]//Proceedings of the 20th International Conference on Advances in Geographic Information Systems.2012:199-208.
[14] XIAN X F,CHEN X J,ZHAO P P,et al.Next point of interest recommendation based on context awareness and personalized metrics[J].Computer Engineering and Science,2018,40(4):616-625.
[15] 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.2011:325-334.
[16] CHENG C,YANG H,KING I,et al.Fused matrix factorization with geographical and social influence in location-based social networks[C]//Twenty-Sixth AAAI Conference on Artificial Intelligence.2012:17-23.
[17] LIU B,FU Y,YAO Z,et al.Learning geographical preferences for point-of-interest recommendation[C]//19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2013:1043-1051.
[18] ZHANG J D,CHOW C Y.GeoSoCa:exploiting geographical,social and categorical correlations for point-of-interest recommendations[C]//38th International ACM SIGIR Conference on Research and Development in Information Retrieval.2015:443-452.
[19] YANG D Q,ZHANG D Q,ZHENG V W,et al.Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2015,45(1):129-142.
[20] HU L K,SUN A X,LIU Y.Your neighbors affect your ratings:on geographical neighborhood influence to rating prediction[C]//Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval.2014:345-354.
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