基于图卷积网络融合群组关系的社会化推荐方法

doi:10.19678/j.issn.1000-3428.0064216

计算机工程 ›› 2023, Vol. 49 ›› Issue (5): 112-121. doi: 10.19678/j.issn.1000-3428.0064216

基于图卷积网络融合群组关系的社会化推荐方法

陈昱瑾^1,2,3, 王晶^1,2,3, 武志昊^1,2,3, 赵耀帅^3,4, 林友芳^1,2,3

1. 北京交通大学计算机与信息技术学院, 北京 100044;
2. 交通数据分析与挖掘北京市重点实验室, 北京 100044;
3. 民航旅客服务智能化应用技术重点实验室, 北京 101318;
4. 中国民航信息网络股份有限公司, 北京 101318

收稿日期:2022-03-17 修回日期:2022-05-17 发布日期:2022-08-19
作者简介:陈昱瑾(1998-),女,硕士研究生,主研方向为深度学习、推荐系统;王晶、武志昊,副教授、博士生导师;赵耀帅(通信作者),高级工程师、硕士;林友芳,教授、博士生导师。
基金资助:
中国民航信息网络股份有限公司与民航旅客服务智能化应用技术重点实验室基金项目（K20L00070）。

Social Recommendation Method Integrating Group Relationships Based on Graph Convolution Network

CHEN Yujin^1,2,3, WANG Jing^1,2,3, WU Zhihao^1,2,3, ZHAO Yaoshuai^3,4, LIN Youfang^1,2,3

1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
2. Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing 100044, China;
3. Key Laboratory of Intelligent Application Technology for Civil Aviation Passenger Services, Beijing 101318, China;
4. TravelSky Technology Limited, Beijing 101318, China

Received:2022-03-17 Revised:2022-05-17 Published:2022-08-19

摘要/Abstract

摘要： 协同过滤推荐系统普遍面临交互数据稀疏，社会化推荐通过引入用户社交信息来缓解数据稀疏问题。现有社会化推荐方法主要关注好友关系，即用户间形成的直接社交关系，但社交数据的稀疏性限制了该类方法的性能表现。由用户加入兴趣小组所形成的群组关系数量繁多且富有价值，然而目前较少有研究关注这种关系，仅有的方法多采用矩阵分解等传统方法建模，对用户协同兴趣和社交影响的表达不够深入。为提升推荐质量，进一步研究群组关系，从缓解社交数据稀疏性的角度论证其在辅助推荐方面的作用，并基于建模能力更强的图卷积网络学习用户、项目与群组之间的高阶关系，分别设计出以间接和直接方式利用群组关系的推荐方法IGRec-Trans和IGRec-Direc，探索更合理的群组关系融合方式。在真实数据集上的实验结果表明，所提方法能有效提升推荐性能，相比最优基准方法DiffNet++，在HR@10和NDCG@10指标上最高可提升4.55%和3.98%，在冷启动用户推荐任务上NDCG@10指标最高可提升18.6%。

Abstract: Collaborative filtering recommender systems typically experience the problem of interaction data sparsity. The social recommendation is proposed to alleviate this issue by introducing users' social information. Existing social recommendation studies primarily focused on the direct connections between users， such as friendship and correlation between users. However，the sparsity of social data limits the performance of these recommender systems. It should be noted that the user-group relationship，another type of valuable information in social networks， which is formed by users joining the interest groups they are interested in， has not received widespread attention. The few existing group-information-enhanced methods have only attempted to integrate it using traditional methods， such as matrix factorization. The collaborative interests and social influence of users have not been modeled satisfactorily. It further studies the user-group relationship to improve recommendation performance， demonstrates its effectiveness to assist in the recommendation from the perspective of alleviating the sparsity of social data， and employs a more robust high-order GCN-based model to learn the representation of users， items， and groups. Specifically， this study proposes two GCN-based methods called IGRec-Trans and IGRec-Direc，which integrate user-group relationships into a social recommendation in indirect and direct manners， respectively. The experimental results for real datasets show that compared with the optimal DiffNet++ model in the benchmark methods， the better method between the two methods improves HR@10 and NDCG@10 by up to 4.55% and 3.98%， respectively. In addition， NDCG@10 can be improved by up to 18.6% on a cold-start recommendation.

Key words: social recommendation, user-group relationship, Graph Convolution Network（GCN）, representation learning, attention mechanism

中图分类号:

TP391

陈昱瑾, 王晶, 武志昊, 赵耀帅, 林友芳. 基于图卷积网络融合群组关系的社会化推荐方法[J]. 计算机工程, 2023, 49(5): 112-121.

CHEN Yujin, WANG Jing, WU Zhihao, ZHAO Yaoshuai, LIN Youfang. Social Recommendation Method Integrating Group Relationships Based on Graph Convolution Network[J]. Computer Engineering, 2023, 49(5): 112-121.

http://www.ecice06.com/CN/Y2023/V49/I5/112

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参考文献

[1] MNIH A,SALAKHUTDINOV R R.Probabilistic matrix factorization[C]//Proceedings of Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2008:1257-1264.
[2] RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence.New York,USA:ACM Press,2009:452-461.
[3] ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749.
[4] GUO G B,ZHANG J,YORKE-SMITH N.TrustSVD:collaborative filtering with both the explicit and implicit influence of user trust and of item ratings[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence.[S.1.]:AAAI Press,2015:123-129.
[5] MA H,ZHOU D Y,LIU C,et al.Recommender systems with social regularization[C]//Proceedings of the 4th ACM International Conference on Web Search and Data Mining.New York,USA:ACM Press,2011:287-296.
[6] YANG B,LEI Y,LIU J M,et al.Social collaborative filtering by trust[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(8):1633-1647.
[7] JAMALI M,ESTER M.A matrix factorization technique with trust propagation for recommendation in social networks[C]//Proceedings of the 4th ACM Conference on Recommender Systems.New York,USA:ACM Press,2010:135-142.
[8] LIU S W,OUNIS I,MACDONALD C,et al.A heterogeneous graph neural model for cold-start recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM Press,2020:2029-2032.
[9] CHEN Y,KOREN Y,VOLINSKY C.Matrix factorization for recommendation with explicit and implicit feedback[J].Knowledge-Based Systems,2018,158:109-117.
[10] HU Y F,KOREN Y,VOLINSKY C.Collaborative filtering for implicit feedback datasets[C]//Proceedings of the 8th IEEE International Conference on Data Mining.Washington D.C.,USA:IEEE Press,2009:263-272.
[11] FAN W Q,MA Y,LI Q,et al.Graph neural networks for social recommendation[C]//Proceedings of World Wide Web Conference.New York,USA:ACM Press,2019:417-426.
[12] WANG X,HE X N,WANG M,et al.Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM Press,2019:165-174.
[13] WU L,LI J W,SUN P J,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.
[14] SUN J S,YING R R,JIANG Y C,et al.Leveraging friend and group information to improve social recommender system[J].Electronic Commerce Research,2020,20(1):147-172.
[15] SHI C,HU B B,ZHAO W X,et al.Heterogeneous information network embedding for recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2019,31(2):357-370.
[16] WANG Z K,LIU H Z,DU Y P,et al.Unified embedding model over heterogeneous information network for personalized recommendation[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.New York,USA:ACM Press,2019:3813-3819.
[17] KOREN Y.Factorization meets the neighborhood:a multifaceted collaborative filtering model[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2008:426-434.
[18] LIU D H,LI J,DU B,et al.DAML:dual attention mutual learning between ratings and reviews for item recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York,USA:ACM Press,2019:344-352.
[19] FAN W Q,LI Q,CHENG M.Deep modeling of social relations for recommendation[J].Artificial Intelligence,2018,32(1):158-171.
[20] YING R,HE R N,CHEN K F,et al.Graph convolutional neural networks for Web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York,USA:ACM Press,2018:974-983.
[21] BERG R,KIPF T N,WELLING M.Graph convolutional matrix completion[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York,USA:ACM Press,2018:657-668.
[22] FAN W Q,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.
[23] WU L,SUN P J,FU Y J,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.New York,USA:ACM Press,2019:235-244.
[24] WU Q T,ZHANG H R,GAO X F,et al.Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems[C]//Proceedings of World Wide Web Conference.New York,USA:ACM Press,2019:2091-2102.
[25] 蒋军.考虑群组信息的社会化推荐方法研究[D].合肥:合肥工业大学,2017. JIANG J.Research on social recommendation method considering group information[D].Hefei:Hefei University of Technology,2017.(in Chinese)

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