Session-based recommendation (SBR) aims to predict the user's 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. We also have already launched the proposed techniques to a large-scale e-commercial online service since April 2021, with significant improvements of top-tier business metrics demonstrated in the online experiments on live traffic.

翻译：会话推荐（SBR）旨在基于简短且动态的会话预测用户的下一步行为。近年来，利用各种精心设计的图神经网络（GNN）捕捉项目间成对关系的研究日益增多，这似乎表明设计更复杂的模型是提升实证性能的万能方法。然而，这些模型在模型复杂度呈指数级增长的同时，仅带来相对边际的性能提升。本文对经典基于GNN的会话推荐模型进行了剖析，并通过实验发现，鉴于读出模块在基于GNN的模型中扮演重要角色，某些复杂的GNN传播过程是冗余的。基于此观察，我们直观地提出移除GNN传播部分，同时让读出模块在模型推理过程中承担更多责任。为此，我们提出多层次注意力混合网络（Atten-Mixer），该网络利用概念视图和实例视图的读出机制，在项目转换上实现多层次推理。由于在大型实际推荐系统中枚举所有可能的高层概念不可行，我们进一步融入会话推荐相关的归纳偏差，即局部不变性和固有优先级，以剪枝搜索空间。在三个基准数据集上的实验证明了我们方法的有效性和效率。自2021年4月起，我们已在大型电商在线服务中部署了所提出的技术，在线流量实验显示，关键业务指标显著提升。