Computer Science ›› 2022, Vol. 49 ›› Issue (9): 55-63.doi: 10.11896/jsjkx.210700085

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

Sequence Recommendation Based on Global Enhanced Graph Neural Network

ZHOU Fang-quan, CHENG Wei-qing   

  1. School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Received:2021-07-08 Revised:2021-10-18 Online:2022-09-15 Published:2022-09-09
  • About author:ZHOU Fang-quan,born in 1997,postgraduate.Her main research interests include personalized recommendation and so on.
    CHENG Wei-qing,born in 1972,Ph.D,professor,is a member of China Computer Federation.Her main research interests include network measurement,distributed algorithms,data mining and so on.
  • Supported by:
    National Natural Science Foundation of China(61170322) and Postgraduate Education Reform Project of Jiangsu Province(JGZZ19_038).

Abstract: Most of the existing session based recommendation systems recommend based on the correlation between the last clicked item and the user preference of the current session,and ignore that there may be item transitions related to the current session in other sessions,while these item transitions may also have a certain impact on users' current preferences Hence,it is indispensable to analyze users' preferences comprehensively from the perspective of local session and global session.Furthermore,most of these recommendation systems ignore the importance of location information,whereas items closer to the predicted location may be more relevant to the current user's interests.To solve these problems,this paper proposes a recommendation model based on global enhanced graph neural network with LSTM(GEL-GNN).GEL-GNN aims to predict the behavior of users according to all sessions,and GNN is employed to capture the global and local relationship of the current session,while LSTM is employed to capture the relationship between sessions at the global level.Firstly,users' preferences are to be translated as a combination of conversation interests based on global and local levels through the attention mechanism layer.Then,the distance between the current position and the predicted position is measured with the reverse position information,so that user behavior can be predicted more accurately.A number of experiments are conducted on three real data sets.Experimental results show that GEL-GNN is superior to the existing session-based graph neural network recommendation models.

Key words: Session-based recommendations, Graph neural network, Attention mechanism, Position information

CLC Number: 

  • TP391
[1]SCHEDL M,ZAMANI H,CHEN C W,et al.Current challenges and visions in music recommender systems research[J].International Journal of Multimedia Information Retrieval,2018,7(2):95-116.
[2]GE Y,ZHAO S,ZHOU H,et al.Understanding Echo Chambers in E-commerce Recommender Systems[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.China:ACM,2020:2261-2270.
[3]BERNARDI L,KAMPS J,KISELEVA J,et al.The Continuous Cold Start Problem in e-Commerce Recommender Systems[J].Computer Science,2015,92(2):28002-28007.
[4]KUMAR P,THAKUR R S.Recommendation system tech-niques and related issues:a survey[J].International Journal of Information Technology,2018,10(4):495-501.
[5]LI Z,ZHAO H,LIU Q,et al.Learning from history and pre-sent:Next-item recommendation via discriminatively exploiting user behaviors[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.London:ACM,2018:1734-1743.
[6]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]//Procee-dings of the 10th International Conference on World Wide Web.Hong Kong:ACM,2001:285-295.
[7]SHANI G,HECKERMAN D,BRAFMAN R I,et al.An MDP-based recommender system[J].Journal of Machine Learning Research,2005,6(9):1265-1295.
[8]HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al.Session-based Recommendations with Recurrent Neural Networks[J].arXiv:1511.06939,2015.
[9]TAN Y K,XU X,LIU Y.Improved recurrent neural networks for session-based recommendations[C]//Proceedings of the 1st workshop on deep learning for recommender systems.Boston:ACM,2016:17-22.
[10]TUAN T X,PHUONG T M.3D convolutional networks for session-based recommendation with content features[C]//Proceedings of the eleventh ACM conference on recommender systems.New York,NY,USA:ACM.2017:138-146.
[11]LI J,REN P,CHEN Z,et al.Neural attentive session-based recommendation[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.Singapore:ACM,2017:1419-1428.
[12]TSAI C H,BRUSILOVSKY P,RAHDARI B.Exploring User-Controlled Hybrid Recommendation in a Conference Context[C]//Joint Proceeding of the ACM IUI 2019 Workshops.Los Angeles:[s.n.],2019:1-6.
[13]QIAN Y,ZHANG Y,MA X,et al.EARS:Emotion-aware re-commender system based on hybrid information fusion[J].Information Fusion,2019,46:141-146.
[14]MNIH A,SALAKHUTDINOV R R.Probabilistic matrix fac-torization[J].Advances in Neural Information Processing Systems,2007,20:1257-1264.
[15]KOREN Y,BELL R.Advances in collaborative filtering[Z].Recommender Systems Handbook,2015:77-118.
[16]SHANI G,HECKERMAN D,BRAFMAN R I,et al.An MDP-based recommender system[J].Journal of Machine Learning Research,2005,6(1):1265-1295.
[17]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.2010:811-820.
[18]SUTSKEVER I,VINYALS O,LEQ V.Sequence to sequencelearning with neural networks[C]//Advances in Neural Information Processing Systems.2014:3104-3112.
[19]LIU Q,ZENG Y,MOKHOSI R,et al.STAMP:short-term attention/memory priority model for session-based recommendation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.London:ACM,2018:1831-1839.
[20]WU S,TANG Y,ZHU Y,et al.Session-based recommendation with graph neural networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence.USA:AAAI,2019:346-353.
[21]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Advances in Neural Information Processing Systems.USA:MIT Press,2013:3111-3119.
[22]PEROZZI B,AL-RFOU R,SKIENA S.Deepwalk:Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2014:701-710.
[23]TANG J,QU M,WANG M,et al.Line:Large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web.Italy:ACM,2015:1067-1077.
[24]GROVER A,LESKOVEC J.node2vec:Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.United States:ACM,2016:855-864.
[25]DUVENAUD D,MACLAURIN D,AGUILERA-IPARRAGUIRRE J,et al.Convolutional networks on graphs for learning molecular fingerprints[J].arXiv:1509.09292,2015.
[26]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.
[27]WANG H,XIAO G,HAN N,et al.Session-Based Graph Convolutional ARMA Filter Recommendation Model[J].IEEE Access,2020,8:62053-62064.
[28]LIU E,CHU Y,LUAN L,et al.Mixing-RNN:a recommendation algorithm based on recurrent neural network[C]//International Conference on Knowledge Science,Engineering and Ma-nagement.Athens:Springer,2019:109-117.
[29]HUANG R,WANG N,HAN C,et al.TNAM:A tag-aware neural attention model for Top-N recommendation[J].Neurocomputing,2020,385:1-12.
[30]SONG W,XIAO Z,WANG Y,et al.Session-based social recommendation via dynamic graph attention networks[C]//Procee-dings of the Twelfth ACM International Conference on Web Search and Data Mining.Australia:ACM,2019:555-563.
[31]WU Q,ZHANG H,GAO X,et al.Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems[C]//The World Wide Web Conference.USA:ACM,2019:2091-2102.
[32]MU N,ZHA D,HE Y,et al.Graph Attention Networks for Neural Social Recommendation[C]//2019 IEEE 31st International Conference on Tools with Artificial Intelligence(ICTAI).Portland:IEEE,2019:1320-1327.
[33]TAO Z,WEI Y,WANG X,et al.MGAT:Multimodal Graph Attention Network for Recommendation[J].Information Proces-sing & Management,2020,57(5):102277.
[34]YUAN Z,LIU H,LIU Y,et al.Spatio-Temporal Dual Graph Attention Network for Query-POI Matching[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.China:ACM,2020:629-638.
[35]GREFF K,SRIVASTAVA R K,KOUTNÍK J,et al.LSTM:A search space odyssey[J].IEEE Transactions on Neural Networks and Learning Systems,2016,28(10):2222-2232.
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