Guided Diffusion-based Counterfactual Augmentation for Robust Session-based Recommendation

Understand

Session-based recommendation (SR) models aim to recommend top-K items to a user, based on the user's behaviour during the current session.

• Several SR models are proposed in the literature, however,concerns have been raised about their susceptibility to inherent biases in the training data (observed data) such as popularity bias.
• SR models when trained on the biased training data may encounter performance challenges on out-of-distribution data in real-world scenarios.
• One way to mitigate popularity bias is counterfactual data augmentation.