Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback
This addresses noise issues in news recommendation for users, though it is incremental as it builds on existing feedback methods by incorporating negative feedback.
The paper tackles noise in implicit feedback for news recommendation by proposing DRPN, a denoising neural network that uses both positive and negative feedback to improve accuracy, achieving state-of-the-art performance on a real-world large-scale dataset.
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.