DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning

Chuhan Wu, Fangzhao Wu, Xiangnan He, Yongfeng Huang

Abstract
Click behaviors are widely used for learning news recommendation models, but they are heavily affected by the biases brought by the news display positions. It is important to remove position biases to train unbiased recommendation model and capture unbiased user interest. In this paper, we propose a news recommendation method named DebiasGAN that can effectively alleviate position biases via adversarial learning. The core idea is modeling the personalized effect of position bias on click behaviors in a candidate-aware way, and learning debiased candidate-aware user embeddings from which the position information cannot be discriminated. More specifically, we use a bias-aware click model to capture the effect of position bias on click behaviors, and use a bias-invariant click model with random candidate positions to estimate the ideally unbiased click scores. We apply adversarial learning to the embeddings learned by the two models to help the bias-invariant click model capture debiased user interest. Experimental results on two real-world datasets show that DebiasGAN effectively improves news recommendation by eliminating position biases.
Anthology ID:
2022.findings-emnlp.213
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2933–2938
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.213
DOI:
10.18653/v1/2022.findings-emnlp.213
Bibkey:
Cite (ACL):
Chuhan Wu, Fangzhao Wu, Xiangnan He, and Yongfeng Huang. 2022. DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2933–2938, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning (Wu et al., Findings 2022)
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