  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.
