Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210800149-5.doi: 10.11896/jsjkx.210800149

• Big Data & Data Science • Previous Articles     Next Articles

Novel Method Based on Graph Attentive Aggregation for POI Recommendations

CAI Guo-yong, CHEN Xin-yi, WANG Shun-jie   

  1. College of Computer and Information Security,Guilin University of Electronic Technology,Guilin,Guangxi 541000,China
    Key Laboratory of Guangxi Trusted Software(Guilin University of Electronic Technology),Guilin,Guangxi 541000,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:CAI Guo-yong,born in 1971,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.His main research interests include social media mining,recommend system and sentiment analysis.
  • Supported by:
    Science and Technology Major Project of Guangxi Province(AA19046004) and Guangxi Key Lab of Trusted Software(kx202060).

Abstract: For services on location-based social network(LBSNs),effective point of interest(POI) recommendation has great economic and social utility.However,how to comprehend the position,structure and behavior related information of LBSNs and proceed reasoning for POI recommendation is still a challenge task.To exploit the heterogeneous information on LBSN,a novel graph attentive aggregation model for POI recommendation(POIR-GAT) is proposed,which exploits both users’ social information and POIs’ attributed information.Firstly,POIR-GAT uses social relationship to construct user-user graph,and extracts user feature vector together with user-POI interaction graph.Secondly,it constructs feature matrix based on different attributes of POIs,obtains hidden factors through matrix decomposition,integrates multiple features into POI feature vector,and learns their common influence on user behavior.Finally,it realizes the integration of social factors and POI features recommended model.Extensive experiments on two public datasets show that the proposed POIR-GAT model can effectively integrate users’ social information and POI feature information,and improve the quality of POI recommendation.

Key words: LBSNs, POI recommendation, Graph attention neural network, Feature matrix decomposition

CLC Number: 

  • TP391.3
[1]BAO J,ZHENG Y,WILKIE D,et al.Recommendations in location-based social networks:A survey[J].GeoInformatica,2015,19:525-565.
[2]WU Z H,PAN S R,CHEN F W,et al.A Comprehensive Survey on Graph Neural Networks[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(1):4-24.
[3]FAN W Q,MA Y,LI Q,et al.Graph Neural Networks for Social Recommendation[C]//The World Wide Web Conference.2019:417-426.
[4]DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.2016:3844-3852.
[5]TYLER D,MA Y,TANG J L.Signed Graph Convolutional Networks[C]//2018 IEEE International Conference on Data Mining(ICDM).2018:929-934.
[6]YE M,YIN P F,LEE W C,et al.Exploiting geographical in-fluence for collaborative point-of-interest recommendation[C]//International ACM Sigir Conference on Research & Development in Information Retrieval.2011:325-334.
[7]YIN H Z,SUN Y Z.LCARS:a location-content-aware recom-mender system[C]//Proceedings of the 19th ACM SIGKDD.2013:221-229.
[8]LI H Y,GE Y.Point-of-interest recommendations:learning potential check-ins from friends[C]//Proceedings of the 22th ACM SIGKDD.2016:975-984.
[9]MA H,YANG H X,MICHAEL R L,et al.Sorec:social recommendation using probabilistic matrix factorization[C]//Procee-dings of the 17th ACM Conference on Information and Know-ledge Management.2008:931-940.
[10]YANG B,LEI Y,LIU J M,et al.Social collaborative filtering by trust[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,39(8):1633-1647.
[11]HE X N,LIAO L Z,ZHANG H W,et al.Neural Collaborative Filtering[C]//Proceedings of the 26th International Conference on World Wide Web.2017:173-182.
[12]WANG X,HE X N,NIE L Q,et al.Item silk road:Recommending items from information domains to social users[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval.2017:185-194.
[13]LIU Q,WU S,WANG L,et al.Predicting the next location:A recurrent model with spatial and temporal contexts[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2016.
[14]YU F Q,CUI L Z,GUO W,et al.A Category-Aware Deep Mo-del for Successive POI Recommendation on Sparse Check-in Data[C]//Proceedings of The Web Conference 2020.2020:1264-1274.
[15]CHRISTOFORIDIS G,KEFALAS P,PAPADOPOULOS A,et al.Recommendation of points-of-interest using graph embeddings[C]//2018 IEEE 5th International Conference on Data Science and Advanced Analytics.2018:31-40.
[16]PETER V M,NOAH E F.Network studies of social influence[J].Sociological Methods & Research,1993,22(1):127-151.
[17]WANG S H,TANG J L,WANG Y L,et al.Exploring Hierarchical Structures for Recommender Systems[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(6):1022-1035.
[18]MNIH A,SALAKHUTDINOV R.Probabilistic Matrix Factorization[C]//21th Conference on Neural Information Processing Systems.2007:1257-1264.
[1] WANG Ying-li, JIANG Cong-cong, FENG Xiao-nian, QIAN Tie-yun. Time Aware Point-of-interest Recommendation [J]. Computer Science, 2021, 48(9): 43-49.
[2] MA Li-bo, QIN Xiao-lin. Topic-Location-Category Aware Point-of-interest Recommendation [J]. Computer Science, 2020, 47(9): 81-87.
[3] SU Chang, WU Peng-fei, XIE Xian-zhong, LI Ning. Point of Interest Recommendation Based on User’s Interest and Geographic Factors [J]. Computer Science, 2019, 46(4): 228-234.
[4] LI Xiao-lun and DING Zhi-jun. Group Travel Trip Recommendation Method in LBSNs [J]. Computer Science, 2017, 44(6): 199-205.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!