Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback

TL;DR

This paper introduces DRPN, a neural network model that leverages both positive and negative implicit feedback to improve news recommendation accuracy by effectively denoising noisy user feedback signals.

Contribution

The paper proposes a novel denoising neural network that utilizes both positive and negative implicit feedback, addressing noise issues in news recommendation systems.

Findings

DRPN achieves state-of-the-art performance on large-scale datasets.

Utilizing both feedback types reduces noise impact and improves recommendation accuracy.

The denoising module effectively filters out noisy implicit feedback signals.

Abstract

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.