# How Fair is Your Diffusion Recommender Model? arXiv: https://arxiv.org/abs/2409.04339 ยท HTML: https://mlsanity.com/paper/2409.04339/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Placed in Diffusion recommenders, one of 14 directions in Diffusion models. - Builds on 41 of its 65 references that are in the map. - First posted on arXiv 6 Sep 2024. ## Placed in - Diffusion models / [Diffusion recommenders](https://mlsanity.com/diffusion/diffusion-recommenders/) ## Builds on (most cited first, 30 of 41) - [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020) - [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022) - [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017) - [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022) - [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014) - [Equivariant Diffusion for Molecule Generation in 3D](https://mlsanity.com/paper/2203.17003/) (2022) - [DiGress: Discrete Denoising diffusion for graph generation](https://mlsanity.com/paper/2209.14734/) (2022) - [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020) - [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023) - [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018) - [Permutation Invariant Graph Generation via Score-Based Generative Modeling](https://mlsanity.com/paper/2003.00638/) (2020) - [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022) - [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022) - [DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models](https://mlsanity.com/paper/2202.04053/) (2022) - [Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness](https://mlsanity.com/paper/2302.10893/) (2023) - [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019) - [DiffuRec: A Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2304.00686/) (2023) - [Finetuning Text-to-Image Diffusion Models for Fairness](https://mlsanity.com/paper/2311.07604/) (2023) - [Diffusion Recommender Model](https://mlsanity.com/paper/2304.04971/) (2023) - [UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation](https://mlsanity.com/paper/2110.15114/) (2021) - [Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling](https://mlsanity.com/paper/2305.04111/) (2023) - [IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models](https://mlsanity.com/paper/1705.10513/) (2017) - [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019) - [XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2209.02544/) (2022) - [DiffKG: Knowledge Graph Diffusion Model for Recommendation](https://mlsanity.com/paper/2312.16890/) (2023) - [RecBole 2.0: Towards a More Up-to-Date Recommendation Library](https://mlsanity.com/paper/2206.07351/) (2022) - [Plug-in Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2401.02913/) (2024) - [CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems](https://mlsanity.com/paper/2204.08085/) (2022) - [A Diffusion model for POI recommendation](https://mlsanity.com/paper/2304.07041/) (2023) - [GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?](https://mlsanity.com/paper/2310.13833/) (2023) ## Built on by (most cited first, 5 of 5) - [A Survey on Diffusion Models for Recommender Systems](https://mlsanity.com/paper/2409.05033/) (2024) - [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025) - [Which Graph Shift Operator? A Spectral Answer to an Empirical Question](https://mlsanity.com/paper/2602.06557/) (2026) - [FAROS: Fair Graph Generation via Attribute Switching Mechanisms](https://mlsanity.com/paper/2507.03728/) (2025) - [FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalization](https://mlsanity.com/paper/2609.31890/) (2026) ## Abstract (arXiv) Diffusion-based learning has settled as a rising paradigm in generative recommendation, outperforming traditional approaches built upon variational autoencoders and generative adversarial networks. Despite their effectiveness, concerns have been raised that diffusion models - widely adopted in other machine-learning domains - could potentially lead to unfair outcomes, since they are trained to recover data distributions that often encode inherent biases. Motivated by the related literature, and acknowledging the extensive discussion around bias and fairness aspects in recommendation, we propose, to the best of our knowledge, the first empirical study of fairness for DiffRec, chronologically the pioneer technique in diffusion-based recommendation. Our empirical study involves DiffRec and its variant L-DiffRec, tested against nine recommender systems on two benchmarking datasets to assess recommendation utility and fairness from both consumer and provider perspectives. Specifically, we first evaluate the utility and fairness dimensions separately and, then, within a multi-criteria setting to investigate whether, and to what extent, these approaches can achieve a trade-off between the two. While showing worrying trends in alignment with the more general machine-learning literature on diffusion models, our results also indicate promising directions to address the unfairness issue in future work. The source code is available at https://github.com/danielemalitesta/FairDiffRec.