  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.
