# Learning Fair Representations for Recommendation: A Graph-based Perspective

arXiv: https://arxiv.org/abs/2102.09140 · HTML: https://mlsanity.com/paper/2102.09140/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2021.

## Built on by (most cited first, 30 of 50)

- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [UP5: Unbiased Foundation Model for Fairness-aware Recommendation](https://mlsanity.com/paper/2305.12090/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Ensuring User-side Fairness in Dynamic Recommender Systems](https://mlsanity.com/paper/2308.15651/) (2023)
- [FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning](https://mlsanity.com/paper/2206.03200/) (2022)
- [Fairness-Aware Graph Neural Networks: A Survey](https://mlsanity.com/paper/2307.03929/) (2023)
- [Fairness Amidst Non-IID Graph Data: A Literature Review](https://mlsanity.com/paper/2202.07170/) (2022)
- [Unlearning Protected User Attributes in Recommendations with Adversarial Training](https://mlsanity.com/paper/2206.04500/) (2022)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [Comprehensive Fair Meta-learned Recommender System](https://mlsanity.com/paper/2206.04789/) (2022)
- [Improving Recommendation Fairness via Data Augmentation](https://mlsanity.com/paper/2302.06333/) (2023)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Learning for Counterfactual Fairness from Observational Data](https://mlsanity.com/paper/2307.08232/) (2023)
- [Multimodality Invariant Learning for Multimedia-Based New Item Recommendation](https://mlsanity.com/paper/2405.15783/) (2024)
- [FairGen: Towards Fair Graph Generation](https://mlsanity.com/paper/2303.17743/) (2023)
- [Graph Learning with Localized Neighborhood Fairness](https://mlsanity.com/paper/2212.12040/) (2022)
- [Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior](https://mlsanity.com/paper/2406.09513/) (2024)
- [Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias](https://mlsanity.com/paper/2405.20718/) (2024)
- [An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering](https://mlsanity.com/paper/2112.07191/) (2021)
- [Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective](https://mlsanity.com/paper/2301.11535/) (2023)
- [Intersectional Two-sided Fairness in Recommendation](https://mlsanity.com/paper/2402.02816/) (2024)
- [FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning](https://mlsanity.com/paper/2410.17555/) (2024)
- [Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder](https://mlsanity.com/paper/2204.00536/) (2022)
- [FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data](https://mlsanity.com/paper/2212.06750/) (2022)
- [Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System](https://mlsanity.com/paper/2507.02000/) (2025)
- [Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis](https://mlsanity.com/paper/2406.03064/) (2024)
- [Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization](https://mlsanity.com/paper/2502.13180/) (2025)

## Abstract (arXiv)

As a key application of artificial intelligence, recommender systems are among the most pervasive computer aided systems to help users find potential items of interests. Recently, researchers paid considerable attention to fairness issues for artificial intelligence applications. Most of these approaches assumed independence of instances, and designed sophisticated models to eliminate the sensitive information to facilitate fairness. However, recommender systems differ greatly from these approaches as users and items naturally form a user-item bipartite graph, and are collaboratively correlated in the graph structure. In this paper, we propose a novel graph based technique for ensuring fairness of any recommendation models. Here, the fairness requirements refer to not exposing sensitive feature set in the user modeling process. Specifically, given the original embeddings from any recommendation models, we learn a composition of filters that transform each user's and each item's original embeddings into a filtered embedding space based on the sensitive feature set. For each user, this transformation is achieved under the adversarial learning of a user-centric graph, in order to obfuscate each sensitive feature between both the filtered user embedding and the sub graph structures of this user. Finally, extensive experimental results clearly show the effectiveness of our proposed model for fair recommendation. We publish the source code at https://github.com/newlei/FairGo.