# Are Big Recommendation Models Fair to Cold Users?

arXiv: https://arxiv.org/abs/2202.13607 · HTML: https://mlsanity.com/paper/2202.13607/

## Key facts

- Cited by 4 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 28 Feb 2022.

## Built on by (most cited first, 4 of 4)

- [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)
- [EB-NeRD: A Large-Scale Dataset for News Recommendation](https://mlsanity.com/paper/2410.03432/) (2024)
- [When to Ask a Question: Understanding Communication Strategies in Generative AI Tools](https://mlsanity.com/paper/2605.11240/) (2026)

## Abstract (arXiv)

Big models are widely used by online recommender systems to boost recommendation performance. They are usually learned on historical user behavior data to infer user interest and predict future user behaviors (e.g., clicks). In fact, the behaviors of heavy users with more historical behaviors can usually provide richer clues than cold users in interest modeling and future behavior prediction. Big models may favor heavy users by learning more from their behavior patterns and bring unfairness to cold users. In this paper, we study whether big recommendation models are fair to cold users. We empirically demonstrate that optimizing the overall performance of big recommendation models may lead to unfairness to cold users in terms of performance degradation. To solve this problem, we propose a BigFair method based on self-distillation, which uses the model predictions on original user data as a teacher to regularize predictions on augmented data with randomly dropped user behaviors, which can encourage the model to fairly capture interest distributions of heavy and cold users. Experiments on two datasets show that BigFair can effectively improve the performance fairness of big recommendation models on cold users without harming the performance on heavy users.