# Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?

arXiv: https://arxiv.org/abs/2105.12353 · HTML: https://mlsanity.com/paper/2105.12353/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2021.

## Built on by (most cited first, 7 of 7)

- [Embarrassingly Simple Text Watermarks](https://mlsanity.com/paper/2310.08920/) (2023)
- [Retrieving Black-box Optimal Images from External Databases](https://mlsanity.com/paper/2112.14921/) (2021)
- [Making Translators Privacy-aware on the User's Side](https://mlsanity.com/paper/2312.04068/) (2023)
- [Overhead-free User-side Recommender Systems](https://mlsanity.com/paper/2411.07589/) (2024)
- [Solving the Cold Start Problem on One's Own as an End User via Preference Transfer](https://mlsanity.com/paper/2502.12398/) (2025)
- [Active Learning from the Web](https://mlsanity.com/paper/2210.08205/) (2022)
- [AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side](https://mlsanity.com/paper/2609.31166/) (2026)

## Abstract (arXiv)

Fairness is a crucial property in recommender systems. Although some online services have adopted fairness aware systems recently, many other services have not adopted them yet. In this work, we propose methods to enable the users to build their own fair recommender systems. Our methods can generate fair recommendations even when the service does not (or cannot) provide fair recommender systems. The key challenge is that a user does not have access to the log data of other users or the latent representations of items. This restriction prohibits us from adopting existing methods designed for service providers. The main idea is that a user has access to unfair recommendations shown by the service provider. Our methods leverage the outputs of an unfair recommender system to construct a new fair recommender system. We empirically validate that our proposed method improves fairness substantially without harming much performance of the original unfair system.