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

Abstract: Aiming at the problems that the existing self-supervised contrastive tasks failed to make full use of the rich semantics in the original data and lacked versatility, a new Session-based Recommendation with Graph Auxiliary Learning (SR-GAL) was proposed. First, an encoding channel with representation consistency was introduced on top of the Graph Neural Network (GNN) to mine more valuable self-supervised signals from raw data. Secondly, in order to make full use of these self-supervised signals, two auxiliary tasks, predictive and constraint, that were closely related to the target task were designed. Finally, a simple and GNN model-agnostic auxiliary learning framework was developed to unify the two auxiliary tasks with the recommendation task to improve the recommendation performance of the GNN model. Experimental results on multiple real datasets show that the SR-GAL model outperforms more advanced models and has good extensibility and versatility. Compared with the suboptimal comparison model CGSNet (Contrastive Graph Self-attention Network), on Diginetica dataset, the precision P@20 and the mean reciprocal ranks MRR@20 increased by 0.58% and 1.61%; on the Tmall dataset, P@20 and MRR@20 increased by 12.65% and 8.41% respectively, verifying the effectiveness of the model.

Key words: recommendation system, session-based recommendation, graph neural network, auxiliary task, self-supervised learning