# Selective Fairness in Recommendation via Prompts

arXiv: https://arxiv.org/abs/2205.04682 · HTML: https://mlsanity.com/paper/2205.04682/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2022.

## Built on by (most cited first, 16 of 16)

- [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)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Fairness Definitions in Language Models Explained](https://mlsanity.com/paper/2407.18454/) (2024)
- [Endowing Pre-trained Graph Models with Provable Fairness](https://mlsanity.com/paper/2402.12161/) (2024)
- [Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective](https://mlsanity.com/paper/2301.11535/) (2023)
- [FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning](https://mlsanity.com/paper/2410.17555/) (2024)
- [Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation](https://mlsanity.com/paper/2305.09330/) (2023)
- [Automatic Feature Fairness in Recommendation via Adversaries](https://mlsanity.com/paper/2309.15418/) (2023)
- [BiVRec: Bidirectional View-based Multimodal Sequential Recommendation](https://mlsanity.com/paper/2402.17334/) (2024)
- [Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model](https://mlsanity.com/paper/2506.19777/) (2025)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters](https://mlsanity.com/paper/2603.23780/) (2026)
- [Customized Conversational Recommender Systems](https://mlsanity.com/paper/2207.00814/) (2022)
- [Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendation](https://mlsanity.com/paper/2601.20848/) (2026)

## Abstract (arXiv)

Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which of and when these user attributes should be considered in fairness-aware modeling should depend on users' specific demands. In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free. We propose a novel parameter-efficient prompt-based fairness-aware recommendation (PFRec) framework, which relies on attribute-specific prompt-based bias eliminators with adversarial training, enabling selective fairness with different attribute combinations on sequential recommendation. Both task-specific and user-specific prompts are considered. We conduct extensive evaluations to verify PFRec's superiority in selective fairness. The source codes are released in urlhttps://github.com/wyqing20/PFRec.