论文标题
将神经网络剥夺新闻推荐，并具有正面和负面的隐性反馈
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback
论文作者
论文摘要
新闻推荐与电影或电子商务建议不同，因为人们通常不会对新闻进行评分。因此，新闻的用户反馈始终是隐式的（单击行为，阅读时间等）。不可避免地，隐含的反馈中有一些声音。一方面，用户可以在单击新闻后立即退出，因为他不喜欢新闻内容，而将噪音留在他积极的隐式反馈中。另一方面，可以同时推荐用户多个有趣的新闻，只单击其中一个，在其负面的隐式反馈中产生噪音。相反的隐式反馈可以构建更集成的用户偏好，并互相帮助以最大程度地减少噪声影响。以前的新闻推荐作品仅使用了积极的隐式反馈，并遭受了噪声影响。在本文中，我们提出了一个具有积极和负面的隐性反馈的新闻推荐的神经网络，名为DRPN。 DRPN利用这两种反馈都可以通过模块来推荐，以确定正与隐式反馈以进一步提高性能。现实世界中大规模数据集的实验证明了DRPN的最新性能。
News recommendation is different from movie or e-commercial recommendation as people usually do not grade the news. Therefore, user feedback for news is always implicit (click behavior, reading time, etc). Inevitably, there are noises in implicit feedback. On one hand, the user may exit immediately after clicking the news as he dislikes the news content, leaving the noise in his positive implicit feedback; on the other hand, the user may be recommended multiple interesting news at the same time and only click one of them, producing the noise in his negative implicit feedback. Opposite implicit feedback could construct more integrated user preferences and help each other to minimize the noise influence. Previous works on news recommendation only used positive implicit feedback and suffered from the noise impact. In this paper, we propose a denoising neural network for news recommendation with positive and negative implicit feedback, named DRPN. DRPN utilizes both feedback for recommendation with a module to denoise both positive and negative implicit feedback to further enhance the performance. Experiments on the real-world large-scale dataset demonstrate the state-of-the-art performance of DRPN.
