Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230100019-9.doi: 10.11896/jsjkx.230100019

• Big Data & Data Science • Previous Articles     Next Articles

Graph Neural Network Recommendation Algorithm Based on Item Relations

LIAO Dong, YU Haizheng   

  1. College of Mathematics and System Sciences,Xinjiang University,Urumqi 830017,China
  • Published:2023-11-09
  • About author:LIAO Dong,born in 1996,postgra-duate.Her main research interestis personalized intelligent recommendation systems.
    YU Haizheng,born in 1976,Ph.D,associate professor,master supervisor,is a member of China Computer Federation.His main research interests include big data and so on.
  • Supported by:
    National Natural Science Foundation of China(61662079,11761070,U1703262) and Natural Science Foundation of Xinjiang,China(2021D01C078).

PDF (PC)

460

Abstract: Typical social recommendation methods are limited by modeling user behavior,such as social behavior between users,interaction behavior between users and items.However,the potential correlation between multiple items that users are interested in is ignored,leading to information loss.In recommendation scenarios with sparse data,the sparsity of user behavior leads to insufficient information available in the system,so it is necessary to introduce item relationships with rich connotations as auxiliary information.This papaer aims to integrate user behavior and auxiliary information to jointly model user preferences,so as to improve the accuracy of recommendations.Most of the data in the recommendation system can be expressed as a graph structure,such as user’s social behavior,user’s interactive behavior and item relationship,which can be converted into user-user graph,user-item graph and item-item graph.Graph neural networks(GNN) are effective in processing large-scale graphic data,and building a framework with item relations based-GNN for social recommendations is facing considerable challenges:1)the item-item relationship is implicit;2)user-user graph,user-item graph,and item-item graph are three different types of graphs;3)the relationship between user and user,user and item,item and item is heterogeneous.In order to solve the above problems,this paper proposes a new social recommendation method based on graph neural network,PEVGraphRec,which introduces a mathematical way to explicitly construct connections between items.Thismodel inherently combines three different graphs to better learn user preference.Finally,an attention mechanism is proposed to consider the weight of different information comprehensively.Comprehensive experiments on three real-world datasets verify the effectiveness of the proposed framework.

Key words: Social recommendation, Graph neural network, Item-Item graph, Heterogeneous, Attention mechanism

CLC Number: 

  • TP183
[1]LIAO J,ZHOU W,LUO F,et al.SocialLGN:Light graph convolution network for social recommendation[J].Information Sciences,2022,589:595-607.
[2]LIU Z W,FAN Z W,WANG Y,et al.Augmenting Sequential Recommendation with Pseudo-PriorItems via Reversely Pre-training Transformer[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021.
[3]LI M N,TEI K J,FUKAZAWA Y.An Efficient Adaptive Attention Neural Network for Social Recommendation[J].IEEE Access,2020,8:63595-63606.
[4]WU L,LI J,SUN P,et al.DiffNet++:A Neural Influence and Interest Diffusion Network for Social Recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2022,34(10):4753-4766.
[5]WU L,SUN P,HONG R,et al.Collaborative neural social re-commendation[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2021,51(1):464-476.
[6]CIALDINI R B,GOLDSTEIN N J.Social influence:Compliance and conformity[J].Annu.Rev.Psychol.2004,55(1):591-621.
[7]MCPHERSON M,SMITH-LOVIN L,COOK J M.Birds of aFeather:Homophily in Social Networks[J].Annual Review of Sociology,2001,27(1):415-444.
[8]RESNICK P,VARIAN H R.Recommender systems[J].Communications of the ACM,1997,40(3):56-58.
[9]TANG J,HU X,LIU H.Social recommendation:a review[J].Social Network Analysis and Mining,2013,3(4):1113-1133.
[10]JAMALI M,ESTER M.A matrix factorization technique withtrust propagation for recommendation in social networks[C]//Proceedings of the fourth ACM conference on Recommender systems.ACM,2010:135-142.
[11]SALAMAT,LUO X,JAFARI A.HeteroGraphRec:A heterogeneous graph-based neural networks for social recommendations[J].Knowledge-Based Systems,2021,217,106817.
[12]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[C]//International Conference onLearning Representations(ICLR).2017.
[13]DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutionalneural networks on graphs with fast localized spectral filtering[C]//30th Conference on Neural Information Proces-sing Systems(NIPS 2016).2016:3844-3852.
[14]BRUNA J,ZAREMBA W,SZLAM A,et al.Spectralnetworksand locally connected networks on graphs[J].arXiv:1312.6203,2013.
[15]ZHANG Y,QI P,MANNING C D.Graph convolutionoverpruned dependency trees improves relation extraction[J].arXiv:1809.10185,2018.
[16]ZHANG Y J,DU Y L,MENG X W.Group recommendation system and its application[J].Chinese Journal of Computers,2016,39(4):745-764.
[17]LIU X,MEI H Y,WANG J H.Research on Graph Neural Network Recommendation Method[J].Computer Engineering and Applications,2022,58(10):41-49.
[18]WANG J C,YUAN F J,CHEN J,et al.Stackrec:Efficient trai-ning of very deep sequential recommender models by iterative stacking.[C]//The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval,Canada.2021:357-366.
[19]LINDEN G,SMITH B,YORK J.Amazon.com recommenda-tions:Item-to-item collaborative filtering[J].IEEE Internet Computing,2003,7(1):76-80.
[20]DESHPANDE M,KARYPIS G.Item-based top-n recommendation algorithms[J].ACM Transactions on Information Systems(TOIS),2004,22(1):143-177.
[21]FAN W,MA Y,LI Q,et al.A Graph Neural Network Framework for Social Recommendations[J].IEEE Transactions on Knowledge and Data Engineering,2022,34(5):2033-2047.
[22]XIANG R,NEVILLE J,ROGATI M.Modeling relationshipstrength in online social networks[C]//Proceedings of the 19th International Conference on World Wide Web.ACM,2010:981-990.
[23]CHEN M,LI Y H,ZHOU X Z.CoNet:Co-occurrence neural networks for recommendation[J].Future Generation Computer Systems,2021,124(3):308-314.
[24]FAN W,MA Y,LI Q,et al.Graph neural networks for social recommendation[C]//Proceedings of the WWW’19.San Francisco,CA,USA,2019:417-426.
[25]ZHANG Z P,GUO X L.PEV:New similarity measure applying to Item-based collaborative filtering algorithm[J].Journal of Chinese Computer Systems,2009,30(4):716-720.
[26]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(WWW ’19).2019:2091-2102.
[27]LIU Z W,ZHENG L,ZHANG J W.JSCN:Joint spectral convolutional network for cross domain recommendation[C]//2019 IEEE International Conference on Big Data(Big Data).IEEE,2019:850-859.
[28]SALAKHUTDINOV R,MNIH A.Probabilistic matrix factorization[C]//Proceedings of the 20th International Conference on Neural Information Processing Systems(NIPS’07).Red Hook,NY,USA,2007:1257-1264.
[29]MA H,YANG H X,LYU R M,et al.Sorec:Social recommendation using probabilistic matrix factorization[C]//Proceedings of the 17th ACM conference on Information and Knowledge Management(CIKM’08).New York,USA,2008:931-940.
[30]MA H,ZHOU D,LIU C,et al.Recommendersystems with social regularization[C]//Proceedings of the fourthACM international conference on Web Search and Data Mining.ACM,2011:287-296.
[31]WU L,SUN P,FU Y,et al.A Neural Influence Diffusion Model for Social Recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR 2019).ACM,Paris,France,2019:235-244.
[32]LI M,TEI K,FUKAZAWA Y.An efficient co-attention neural network for social recommendation[C]//Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence(WI ’19).Thessaloniki,Greece,2019:34-42.
[33]BERG R,KIPF T N,WELLING M.Graph convolutional matrix completion[C]//Proceedings of the KDD’18 Deep Learning Day.London,UK,2018.
[34]GROVER A,LESKOWEC J.node2vec:Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD’16).San Francisco,CA,USA,2016:855-864.
[35]YANG L,LIU Z,DOU Y,et al.ConsisRec:Enhancing GNN for social recommendation via consistent neighbor aggregation[C]//The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR’21).ACM,2021.
[1] LI Ke, YANG Ling, ZHAO Yanbo, CHEN Yonglong, LUO Shouxi. EGCN-CeDML:A Distributed Machine Learning Framework for Vehicle Driving Behavior Prediction [J]. Computer Science, 2023, 50(9): 318-330.
[2] YI Qiuhua, GAO Haoran, CHEN Xinqi, KONG Xiangjie. Human Mobility Pattern Prior Knowledge Based POI Recommendation [J]. Computer Science, 2023, 50(9): 139-144.
[3] YI Liu, GENG Xinyu, BAI Jing. Hierarchical Multi-label Text Classification Algorithm Based on Parallel Convolutional Network Information Fusion [J]. Computer Science, 2023, 50(9): 278-286.
[4] LUO Yuanyuan, YANG Chunming, LI Bo, ZHANG Hui, ZHAO Xujian. Chinese Medical Named Entity Recognition Method Incorporating Machine ReadingComprehension [J]. Computer Science, 2023, 50(9): 287-294.
[5] DU Ming, YANG Wen, ZHOU Junfeng. Maximum Influential Community Search in Heterogeneous Information Network [J]. Computer Science, 2023, 50(8): 16-26.
[6] ZHANG Yian, YANG Ying, REN Gang, WANG Gang. Study on Multimodal Online Reviews Helpfulness Prediction Based on Attention Mechanism [J]. Computer Science, 2023, 50(8): 37-44.
[7] TENG Sihang, WANG Lie, LI Ya. Non-autoregressive Transformer Chinese Speech Recognition Incorporating Pronunciation- Character Representation Conversion [J]. Computer Science, 2023, 50(8): 111-117.
[8] WANG Jiahao, ZHONG Xin, LI Wenxiong, ZHAO Dexin. Human Activity Recognition with Meta-learning and Attention [J]. Computer Science, 2023, 50(8): 193-201.
[9] WANG Yu, WANG Zuchao, PAN Rui. Survey of DGA Domain Name Detection Based on Character Feature [J]. Computer Science, 2023, 50(8): 251-259.
[10] SHAN Xiaohuan, SONG Rui, LI Haihai, SONG Baoyan. Event Recommendation Method with Multi-factor Feature Fusion in EBSN [J]. Computer Science, 2023, 50(7): 60-65.
[11] YAN Mingqiang, YU Pengfei, LI Haiyan, LI Hongsong. Arbitrary Image Style Transfer with Consistent Semantic Style [J]. Computer Science, 2023, 50(7): 129-136.
[12] JIANG Linpu, CHEN Kejia. Self-supervised Dynamic Graph Representation Learning Approach Based on Contrastive Prediction [J]. Computer Science, 2023, 50(7): 207-212.
[13] LI Rongchang, ZHENG Haibin, ZHAO Wenhong, CHEN Jinyin. Data Reconstruction Attack for Vertical Graph Federated Learning [J]. Computer Science, 2023, 50(7): 332-338.
[14] ZHANG Shunyao, LI Huawang, ZHANG Yonghe, WANG Xinyu, DING Guopeng. Image Retrieval Based on Independent Attention Mechanism [J]. Computer Science, 2023, 50(6A): 220300092-6.
[15] LIU Haowei, YAO Jingchi, LIU Bo, BI Xiuli, XIAO Bin. Two-stage Method for Restoration of Heritage Images Based on Muti-scale Attention Mechanism [J]. Computer Science, 2023, 50(6A): 220600129-8.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.