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：多数会话型推荐系统（SBRSs）侧重于从用户当前会话中已观测的项目中提取信息以预测下一个项目，忽略了会话外部影响用户选择项目的原因（称为会话外原因，OSCs）。然而，这些原因在现实世界中广泛存在，且少有研究探究其在SBRSs中的作用。本文从因果推断的视角分析了SBRSs中OSCs的因果关系与相关性。我们发现OSCs本质上是SBRSs中的混杂因素，这会导致训练SBRS模型所使用的数据中出现虚假相关。为解决该问题，我们提出了一种新型SBRS框架——COCO-SBRS（反事实协作会话型推荐系统），用于学习SBRSs中OSCs与用户-项目交互之间的因果关系。COCO-SBRS首先采用自监督方法，通过为数据中用户每次选择项目的行为设计原因的伪标签来预训练推荐模型，以指导训练过程。随后，COCO-SBRS利用反事实推断，基于预训练推荐模型的输出结合因果考量进行项目推荐，以缓解数据稀疏性问题。由此，COCO-SBRS能够学习数据中的因果关系，防止模型学习到虚假相关。我们在三个真实世界数据集上进行的大量实验结果表明，所提框架相较于十个代表性SBRSs具有优越性。