SCM4SR: Structural Causal Model-based Data Augmentation for Robust Session-based Recommendation

With mounting privacy concerns, and movement towards a cookieless internet, session-based recommendation (SR) models are gaining popularity. The goal of SR models is to recommend top-K items to a user by utilizing information from past actions within a session. Many deep neural networks (DNN) based SR have been proposed in the literature, however, they experience performance declines in practice due to inherent biases (e.g., popularity bias) present in training data. To alleviate this, we propose an underlying neural-network (NN) based Structural Causal Model (SCM) which comprises an evolving user behavior (simulator) and recommendation model. The causal relations between the two sub-models and variables at consecutive timesteps are defined by a sequence of structural equations, whose parameters are learned using logged data. The learned SCM enables the simulation of a user's response on a counterfactual list of recommended items (slate). For this, we intervene on recommendation slates with counterfactual slates and simulate the user's response through learned SCM thereby generating counterfactual sessions to augment the training data. Through extensive empirical evaluation on simulated and real-world datasets, we show that the augmented data mitigates the impact of sparse training data and improves the performance of the SR models.