Session-based recommendation (SBR) aims to predict the user next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various elaborately designed graph neural networks (GNNs) to capture the pair-wise relationships among items, seemingly suggesting the design of more complicated models is the panacea for improving the empirical performance. However, these models achieve relatively marginal improvements with exponential growth in model complexity. In this paper, we dissect the classical GNN-based SBR models and empirically find that some sophisticated GNN propagations are redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process. To this end, we propose the Multi-Level Attention Mixture Network (Atten-Mixer), which leverages both concept-view and instance-view readouts to achieve multi-level reasoning over item transitions. As simply enumerating all possible high-level concepts is infeasible for large real-world recommender systems, we further incorporate SBR-related inductive biases, i.e., local invariance and inherent priority to prune the search space. Experiments on three benchmarks demonstrate the effectiveness and efficiency of our proposal.
翻译:基于会议的建议(SBR)旨在预测用户下一个基于短会议和动态会议的下一步行动。最近,人们越来越有兴趣利用各种精心设计的图形神经网络(GNN)来捕捉各项目之间的双向关系,似乎建议设计更复杂的模型是改进实验性表现的灵丹妙药。然而,这些模型在模型复杂性的指数增长中取得了相对微不足道的改进。在本文件中,我们分解了以GNNN为基础的经典SBR模型,从经验上发现一些复杂的GNN的传播是多余的,因为读出模块在基于GNNN的模型中起着重要作用。基于这一观察,我们直觉地提议删除GNN传播部分,而读出模块将在模型推理过程中承担更多的责任。为此,我们提议多层次关注像素网络(Aten-Mixer),利用概念-视野和实例浏览读出实现关于项目转换的多层次推理。由于所有可能的高层次概念都无法在基于GNNN的模型模型模型模型中发挥重要作用。基于这一观察,因此,我们无意地提议取消GNNNN传播部分,而在模型推介部分中将SBRAnial-lavial-lavial Prial vial vial vial vial vial lavial vial vial vialal vial vialalalalal vialalal viol