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

arXiv: https://arxiv.org/abs/2410.21892 · HTML: https://mlsanity.com/paper/2410.21892/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion recommenders, one of 14 directions in Diffusion models.
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Placed in

- Diffusion models / [Diffusion recommenders](https://mlsanity.com/diffusion/diffusion-recommenders/)

## Builds on (most cited first, 10 of 10)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion](https://mlsanity.com/paper/2310.20453/) (2023)
- [CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation](https://mlsanity.com/paper/2109.05261/) (2021)
- [DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](https://mlsanity.com/paper/1909.03602/) (2019)
- [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2005.02844/) (2020)
- [Top-N Recommendation with Counterfactual User Preference Simulation](https://mlsanity.com/paper/2109.02444/) (2021)

## Built on by (most cited first, 1 of 1)

- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)

## Abstract (arXiv)

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.