Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2711-2718.DOI: 10.11772/j.issn.1001-9081.2023091257

• Data science and technology • Previous Articles     Next Articles

Session-based recommendation with graph auxiliary learning

Tingjie TANG1,2, Jiajin HUANG3(), Jin QIN1,2   

  1. 1.State Key Laboratory of Public Big Data (Guizhou University),Guiyang Guizhou 550025,China
    2.College of Computer Science and Technology,Guizhou University,Guiyang Guizhou 550025,China
    3.Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China
  • Received:2023-09-13 Revised:2023-12-14 Accepted:2023-12-15 Online:2024-03-19 Published:2024-09-10
  • Contact: Jiajin HUANG
  • About author:TANG Tingjie, born in 1999, M. S. candidate. His research interests include recommender system.
    QIN Jin, born in 1978, Ph. D., associate professor. His research interests include reinforcement learning, computational intelligence.
  • Supported by:
    Guizhou Province Science and Technology Program (Qiankehe Basic[2020]1Y275)

基于图辅助学习的会话推荐

唐廷杰1,2, 黄佳进3(), 秦进1,2   

  1. 1.公共大数据国家重点实验室(贵州大学), 贵阳 550025
    2.贵州大学 计算机科学与技术学院, 贵阳 550025
    3.北京工业大学 信息学部, 北京 100124
  • 通讯作者: 黄佳进
  • 作者简介:唐廷杰(1999—),男,贵州黔东南人,硕士研究生,主要研究方向:推荐系统
    黄佳进(1977—),男,贵州遵义人,助理研究员,博士,主要研究方向:推荐系统
    秦进(1978—),男,贵州毕节人,副教授,博士,主要研究方向:强化学习、计算智能。
  • 基金资助:
    贵州省科技计划项目(黔科合基础[2020]1Y275)

Abstract:

Aiming at the problems that the existing self-supervised contrastive tasks fail to make full use of the rich semantic information in the original data and lack universality, a Session-based Recommendation with Graph Auxiliary Learning (SR-GAL) model was proposed. Firstly, an encoding channel with Representation Consistency (RC) was introduced on the basis of Graph Neural Network (GNN) to mine more valuable self-supervised signals from the original data. Secondly, in order to make full use of these self-supervised signals, two auxiliary tasks, predictive one and constraint one, that were closely related to the target task were designed. Finally, a simple and GNN model-unrelated auxiliary learning framework was developed to unify the two auxiliary tasks with the recommendation task in order to improve the recommendation performance of the GNN model. Compared with the suboptimal comparison model CGSNet (Contrastive Graph Self-attention Network), on Diginetica dataset, the proposed model has the Precision P@20 and Mean Reciprocal Rank MRR@20 increased by 0.58% and 1.61%; on Tmall dataset, the proposed model has the P@20 and MRR@20 increased by 12.65% and 8.41% respectively, verifying the effectiveness of the model. Experimental results on multiple real datasets show that SR-GAL model outperforms advanced models and has good extensibility as well as universality.

Key words: recommender system, session-based recommendation, Graph Neural Network (GNN), auxiliary task, self-supervised learning

摘要:

针对现有的自监督对比任务未能充分利用原始数据中的丰富语义以及缺乏通用性的问题,提出一种基于图辅助学习的会话推荐(SR-GAL)模型。首先,在图神经网络(GNN)的基础上引入具有表示一致性(RC)的编码通道,从原始数据中挖掘更有价值的自监督信号;其次,为了充分利用这些自监督信号,设计了与目标任务关系紧密的预测性辅助任务和约束性辅助任务;最后,开发了一个简单且与GNN模型无关的辅助学习框架,将两个辅助任务与推荐任务统一起来,从而提高GNN模型的推荐性能。与次优的对比模型CGSNet(Contrastive Graph Self-attention Network)相比,在Diginetica数据集上,所提模型的精确率P@20和平均倒数排名MRR@20提升了0.58%和1.61%;在Tmall数据集上,所提的模型的P@20和MRR@20分别提升了12.65%和8.41%,验证了该模型的有效性。在多个真实数据集上的实验结果表明,SR-GAL模型优于较先进的模型,并且具有良好的可扩展性和通用性。

关键词: 推荐系统, 会话推荐, 图神经网络, 辅助任务, 自监督学习

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