Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence the user's selection of items. However, these causes widely exist in the real world, and few studies have investigated their role in SBRSs. In this work, we analyze the causalities and correlations of the OSCs in SBRSs from the perspective of causal inference. We find that the OSCs are essentially the confounders in SBRSs, which leads to spurious correlations in the data used to train SBRS models. To address this problem, we propose a novel SBRS framework named COCO-SBRS (COunterfactual COllaborative Session-Based Recommender Systems) to learn the causality between OSCs and user-item interactions in SBRSs. COCO-SBRS first adopts a self-supervised approach to pre-train a recommendation model by designing pseudo-labels of causes for each user's selection of the item in data to guide the training process. Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem. As a result, COCO-SBRS can learn the causalities in data, preventing the model from learning spurious correlations. The experimental results of our extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed framework over ten representative SBRSs.

翻译：大多数基于会话的推荐系统（SBRS）专注于从用户当前会话中观测到的物品中提取信息以预测下一个物品，忽略了影响用户物品选择的会话外部因素（即会话外成因，OSCs）。然而，这些因素在现实世界中普遍存在，且鲜有研究探讨其在SBRS中的作用。本文从因果推断的视角分析了SBRS中OSCs的因果关系与相关性，发现OSCs本质上是SBRS中的混杂因子，会导致训练SBRS模型的数据产生虚假关联。为解决该问题，我们提出了一种新型SBRS框架——COCO-SBRS（基于反事实协同的会话推荐系统），用于学习SBRS中OSCs与用户-物品交互之间的因果关系。COCO-SBRS首先采用自监督方法，通过为数据中每个用户选择的物品设计成因的伪标签来预训练推荐模型，从而引导训练过程。随后，COCO-SBRS利用反事实推理，基于预训练推荐模型的输出，在考虑因果关系的情况下推荐物品，以缓解数据稀疏问题。最终，COCO-SBRS能够学习数据中的因果关系，防止模型学习到虚假关联。在三个真实数据集上开展的大量实验结果表明，本文提出的框架相较于十个代表性SBRS具有显著优越性。