DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning

TL;DR

DebiasGAN is a novel adversarial learning approach that effectively removes position bias from news recommendation models, leading to more accurate user interest prediction.

Contribution

The paper introduces DebiasGAN, a new adversarial learning framework that eliminates position bias in news recommendation systems, improving recommendation accuracy.

Findings

DebiasGAN significantly outperforms baseline models in accuracy.

It effectively reduces the influence of position bias on click behaviors.

Experimental results on real datasets validate its effectiveness.

Abstract

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.