DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
This addresses a specific bias issue in news recommendation systems, offering an incremental improvement for enhancing user experience by more accurately targeting interests.
The paper tackles the problem of position bias in news recommendation, where user clicks are influenced by item placement rather than interest, and proposes DebiasGAN, which uses adversarial learning to eliminate this bias, improving recommendation accuracy on two real-world datasets.
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.