Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (3): 244-252.DOI: 10.3778/j.issn.1002-8331.2204-0245

• Big Data and Cloud Computing • Previous Articles     Next Articles

Self-Supervised Graph Neural Networks for Session-Based Recommendation

WANG Yonggui, ZHAO Xiaoxuan   

  1. College of Electronics and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Online:2023-02-01 Published:2023-02-01



  1. 辽宁工程技术大学 电子与信息工程学院,辽宁 葫芦岛 125105

Abstract: Compared with traditional recommendation methods, session-based recommendation methods are more susceptible to the data sparsity problem due to the limited data of short-term user interaction. In order to enhance session data and alleviate the impact of datas parseness on session recommendation performance, a model named self-supervised graph neural networks for session-based recommendation(Ss-GNN) is proposed. It constructs asession graph and establishes a session recommendation task based on graph attention network to obtain the item-level and session-level representation. Then, from the perspective of the session-level representation, it uses the user’s general and current interests to construct an auxiliary task to obtain self-supervised signals.It utilizes self-supervised learning to maximize mutual information between the recommendation task and the auxiliary task to enhance session data and improve recommendation performance. Experiments are conducted on two public data sets, Yoochoose and Tmall. Compared with the baseline model, the proposed model improves at least 0.94% and 0.79% in P@20 and MRR@20 on Yoochoose, 9.61% and 4.67% in P@20 and MRR@20 on Tmall, which proves the effectiveness of Ss-GNN model.

Key words: session recommendation, self-supervised learning, data sparsity, graph neural network

摘要: 基于会话的推荐方法由于短期用户交互数据有限,与传统推荐方法相比,其性能更容易受到数据稀疏性问题的影响。为增强会话数据以缓解数据稀疏对会话推荐性能的影响,提出一种结合自监督学习的图神经网络会话推荐(Ss-GNN)模型。构建会话图并建立基于图注意力网络的会话推荐任务来获取项目级表示和会话级表示;从会话级表示的角度出发,利用用户的一般兴趣和当前兴趣来构建辅助任务获取自监督信号;利用自监督学习实现推荐任务和辅助任务之间的互信息最大化,以增强会话数据,从而提升推荐性能。在Yoochoose和Tmall两个公开数据集上进行实验,与基线模型相比,提出的模型在Yoochoose上P@20和MRR@20至少提升了0.94%和0.79%,在Tmall上P@20和MRR@20至少提升了9.61%和4.67%,证明了Ss-GNN模型的有效性。

关键词: 会话推荐, 自监督学习, 数据稀疏性, 图神经网络