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

TL;DR

This paper introduces a diffusion model-based counterfactual data augmentation method to improve session-based recommendation systems, especially for less popular items, by reducing popularity bias and enhancing recommendation accuracy.

Contribution

It presents a novel guided diffusion-based framework for counterfactual augmentation in SR, outperforming existing methods in mitigating popularity bias and improving recommendation metrics.

Findings

Up to 20% gain in Recall for less popular items.

Up to 13% gain in CTR for less popular items.

Significant improvements over baseline and state-of-the-art augmentation methods.

Abstract

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. Compared to prior works that rely on generating data using SR models, we focus on utilizing the capabilities of state-of-the art diffusion models for generating counterfactual data. We propose a guided diffusion-based counterfactual augmentation framework for SR. Through a combination of offline and online experiments on a real-world and simulated dataset, respectively, we show that our approach performs significantly better than the baseline SR models and other state-of-the art augmentation frameworks. More importantly, our framework shows significant improvement on less popular target items, by achieving up to 20% gain in Recall and 13% gain in CTR on real-world and simulated datasets,respectively.