计算机科学 ›› 2024, Vol. 51 ›› Issue (5): 54-61.doi: 10.11896/jsjkx.230300092

• 数据库&大数据&数据科学 • 上一篇    下一篇

结合图对比学习的多图神经网络会话推荐方法

卢敏, 原子婷   

  1. 1 中国民航大学计算机科学与技术学院 天津 300300
    2 智慧机场理论与系统民航局重点实验室 天津 300300
  • 收稿日期:2023-03-11 修回日期:2023-11-13 出版日期:2024-05-15 发布日期:2024-05-08
  • 通讯作者: 原子婷(3138194302@qq.com)
  • 作者简介:(mlu@cauc.edu.cn)
  • 基金资助:
    中央高校基本科研业务费专项基金(3122021090)

Graph Contrast Learning Based Multi-graph Neural Network for Session-based RecommendationMethod

LU Min, YUAN Ziting   

  1. 1 College of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300,China
    2 Key Laboratory of Smart Airport Theory and System,Civil Aviation Administration of China,Tianjin 300300,China
  • Received:2023-03-11 Revised:2023-11-13 Online:2024-05-15 Published:2024-05-08
  • About author:LU Min,born in 1985,Ph.D,is a member of CCF(No.23698S).His main research interests include web mining and information retrieval.
    YUAN Ziting,born in 1996,postgra-duate.Her main research interests include recommender system and contrastive learning.
  • Supported by:
    Fundamental Research Funds for the Central Universities of Ministry of Education of China(3122021090).

摘要: 会话推荐根据匿名用户短期内的交互数据预测下一个交互物品。针对会话中物品少、物品长尾分布等特性,现有基于图对比学习的会话推荐模型提出对会话内物品采用随机裁剪、扰动等方式构造正负样本。然而,上述随机退出策略进一步缩减较短会话中的可用物品,使得会话更加稀疏,引起会话兴趣学习偏差。为此,提出了结合图对比学习的多图神经网络会话推荐方法。其核心思想是:在物品局部图、物品全局图等上提取融入物品局部和全局的高阶邻域物品表示,并生成物品级的会话表示,然后设计会话-会话图并学习会话级的会话表示,最后递归利用不同级别会话兴趣生成正负样本对,通过对比学习机制增强会话兴趣区分性。与退出策略相比,所提模型保留了完整的会话信息,实现了真正的数据扩充。在两个基准数据集上进行了大量实验,结果表明,该算法的推荐性能远优于主流基线方法。

关键词: 会话推荐, 图对比学习, 图神经网络, 会话兴趣, 正负样本

Abstract: Session recommendation predicts the next interaction item based on anonymous user interaction data over a short pe-riod of time.Sessions have characteristics such as few items and long-tail distribution of items.Existing session recommendation models based on graph contrast learning construct positive and negative samples by randomly cropping and perturbing the items within a session,etc.However,the above random exit strategy further shrinks the available items in shorter sessions.This makes the sessions more sparse and causes session interest learning bias.To this end,a graph contrast learning based multi-graph neural network for session-based recommendation method is proposed.The core idea is as follows:the model extracts item representations on item local graphs as well as item global graphs,incorporating both local and global higher-order neighborhood information of the items.Based on this,the model generates item-level session representations.Then,Session-level session representations are learned on the session-session graph.Finally,the model recursively generates positive and negative sample pairs using diffe-rent levels of conversational interest.And the discriminative nature of the session interests is enhanced by the contrast learning mechanism.Compared with the exit strategy,the proposed model preserves the complete session information and achieves true data expansion.Extensive experiments on two benchmark datasets show that the recommendation performance of the algorithm is much better than that of the mainstream baseline approach.

Key words: Session recommendation, Graph contrast learning, Graph neural networks, Session interest, Positive and negative samples

中图分类号: 

  • TP391
[1]WANG S,CAO L,WANG Y,et al.A survey on session-basedrecommender systems[J].ACM Computing Surveys,2021,54(7):1-38.
[2]QIAO S,ZHOU W,WEN J,et al.Multi-perspective enhanced representation for effective session-based recommendation[J].Knowledge-Based Systems,2023,263(12):110284.
[3]HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al.Ses-sion-based recommendations with recurrent neural networks[J].arXiv:1511.06939,2015.
[4]WU S,TANG Y,ZHU Y,et al.Session-based recommendation with graph neural networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2019,33(1):346-353.
[5]JAISWAL A,BABU A R,ZADEH M Z,et al.A survey on con-trastive self-supervised learning[J].Technologies,2020,9(1):2-23.
[6]ZHU Y Q,XU Y C,YU F,et al.Graph contrastive learning with adaptive augmentation[C]// Proceedings of the Web Conference 2021.New York:ACM,2021:2069-2080.
[7]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37.
[8]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.
[9]LI J,REN P,CHEN Z,et al.Neural attentive session-based re-commendation[C]//Proceedings of the 2017 ACM on Confe-rence on Information and Knowledge Management.2017:1419-1428.
[10]WANG M,REN P,MEI L,et al.A Collaborative Session-based Recommendation Approach with Parallel Memory Modules[C]//Proceedings of the 42nd International ACM SIGIR Confe-rence.ACM,2019:345-354.
[11]XU C,ZHAO P,LIU Y,et al.Graph Contextualized Self-Attention Network for Session-based Recommendation[C]// Proceedings of the 28th International Joint Conference on Artificial Intelligence.Macao,PEOPLES R China, IJCAI.2019,19:3940-3946.
[12]QIU R H,LI J J,HUANG Z,et al.Rethinking the item order in session-based recommendation with graph neural networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.New York:ACM,2019:579-588.
[13]CHEN J F,ZHU G H,HOU H J,et al.AutoGSR:Neural Architecture Search for Graph-based Session Recommendation[C]//Proceedings of The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.Madrid:SPAIN,2022:1694-1704.
[14]YANG L Q,LUO L H,XIN L F,et al.DAGNN:Demand-aware Graph Neural Networks for Session-based Recommendation[J].arXiv:2105.14428v1,2022.
[15]HE K,FAN H,WU Y,et al.Momentum contrast for unsupervised visual representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:9729-9738.
[16]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018.
[17]ZHOU K,WANG H,ZHAO W X,et al.S3-rec:Self-supervised learning for sequential recommendation with mutual information maximization[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management.New York:ACM,2020:1893-1902.
[18]XIE X,SUN F,LIU Z Y,et al.Contrastive learning for sequential recommendation [J].arXiv:2010.14395,2020.
[19]XIA X,YIN H Z,YU J L,et al.Self-supervised hypergraph convolutional networks for session-based recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2021:4503-4511.
[20]XIA L H,HUANG C,XU Y,et al.Hypergraph ContrastiveCollaborative Filtering[C]//Proceedings of The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.Madrid:PAIN,2022:70-79.
[21]PHUONG T M,THANH T C,BACH N X.Combining user-based and session-based recommendations with recurrent neural networks[C]//International Conference on Neural Information Processing.New York:Springer,Cham,2018:487-498.
[22]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.New York:ACM,2001:285-295.
[23]LI J,REN P,CHEN Z,et al.Neural attentive session-based re-commendation[C]//Proceedings of the 2017 ACM on Confe-rence on Information and Knowledge Management.New York:ACM,2017:1419-1428.
[24]LIU Q,ZENG Y F,MOKHOSI R,et al.STAMP:short-term at-tention/memory priority model for session-based recommendation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2018:1831-1839.
[25]LI A S,CHENG Z Y,LIU F,et al.Disentangled Graph Neural Networks for Session-based Recommendation[J].IEEE Tran-sactions on Knowledge and Data Engineering,2022,35(8):1-13.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!