# Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset

arXiv: https://arxiv.org/abs/2303.11574 · HTML: https://mlsanity.com/paper/2303.11574/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [User-Creator Feature Polarization in Recommender Systems with Dual Influence](https://mlsanity.com/paper/2407.14094/) (2024)
- [Epsilon non-Greedy: A Bandit Approach for Unbiased Recommendation via Uniform Data](https://mlsanity.com/paper/2310.04855/) (2023)
- [DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops](https://mlsanity.com/paper/2311.05864/) (2023)
- [Where to Explore: A Reach and Cost-Aware Approach for Unbiased Data Collection in Recommender Systems](https://mlsanity.com/paper/2512.14733/) (2025)
- [Generative Pseudo-Labeling for Pre-Ranking with LLMs](https://mlsanity.com/paper/2602.20995/) (2026)

## Abstract (arXiv)

Debiased recommendation with a randomized dataset has shown very promising results in mitigating the system-induced biases. However, it still lacks more theoretical insights or an ideal optimization objective function compared with the other more well studied route without a randomized dataset. To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to the system-induced biases. Firstly, we formulate a new ideal optimization objective function with a randomized dataset. Secondly, according to the prior constraints that an adopted loss function may satisfy, we derive two different upper bounds of the objective function, i.e., a generalization error bound with the triangle inequality and a generalization error bound with the separability. Thirdly, we show that most existing related methods can be regarded as the insufficient optimization of these two upper bounds. Fourthly, we propose a novel method called debiasing approximate upper bound with a randomized dataset (DUB), which achieves a more sufficient optimization of these upper bounds. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of our DUB.