DebiasGAN：用对抗学习消除新闻推荐中的立场偏见
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
摘要：
新闻推荐对于提高用户的新闻阅读体验具有重要意义。用户的新闻点击行为被广泛用于推断用户的兴趣和预测未来的点击。然而，点击行为受到网页上显示的新闻位置所带来的偏见的严重影响。为了准确地定位用户兴趣，消除位置偏差对推荐模型的影响是很重要的。在本文中，我们提出了一种名为DebiasGAN的新闻推荐方法，可以通过对抗学习有效地消除位置偏差的影响。我们使用一个偏见感知点击模型来捕捉位置偏见对点击行为的影响，我们使用一个偏见不变的点击模型与随机候选新闻位置来估计理想的无偏点击分数。我们将对抗学习技术应用于两个模型学习的隐藏表示，以帮助偏差不变的点击模型捕获用户对新闻的独立于偏差的兴趣。在两个真实数据集上的实验结果表明，DebiasGAN能够有效地消除位置偏差，提高新闻推荐的准确率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
