# DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning

arXiv: https://arxiv.org/abs/2106.06258 · HTML: https://mlsanity.com/paper/2106.06258/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2021.

## Built on by (most cited first, 1 of 1)

- [Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation](https://mlsanity.com/paper/2409.17711/) (2024)

## Abstract (arXiv)

News recommendation is important for improving news reading experience of users. Users' news click behaviors are widely used for inferring user interests and predicting future clicks. However, click behaviors are heavily affected by the biases brought by the positions of news displayed on the webpage. It is important to eliminate the effect of position biases on the recommendation model to accurately target user interests. In this paper, we propose a news recommendation method named DebiasGAN that can effectively eliminate the effect of position biases via adversarial learning. We use a bias-aware click model to capture the influence of position bias on click behaviors, and we use a bias-invariant click model with random candidate news positions to estimate the ideally unbiased click scores. We apply adversarial learning techniques to the hidden representations learned by the two models to help the bias-invariant click model capture the bias-independent interest of users on news. Experimental results on two real-world datasets show that DebiasGAN can effectively improve the accuracy of news recommendation by eliminating position biases.