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

• URL: http://arxiv.org/abs/2204.04397v1
• Date: Sat, 9 Apr 2022 05:47:17 GMT
• Title: Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback
• Authors: Yunfan Hu and Zhaopeng Qiu and Xian Wu
• Abstract summary: 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.
• Score: 32.87091195673515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence [54.13541697801396]
  We propose a new task named Proactive Recommendation in Social Networks (PRSN) PRSN indirectly steers users' interest by utilizing the influence of social neighbors. We propose a Neighbor Interference Recommendation (NIRec) framework with two key modules.
  arXiv  Detail & Related papers  (2024-09-13T15:53:40Z)
• DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback [51.72177873832969]
  We propose a novel model that models positive and negative feedback from a frequency filter perspective. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of the proposed model.
  arXiv  Detail & Related papers  (2024-05-24T07:07:41Z)
• Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders [13.762960304406016]
  We introduce explicit and implicit negative user feedback into the training objective of sequential recommenders. We demonstrate the effectiveness of this approach using live experiments on a large-scale industrial recommender system.
  arXiv  Detail & Related papers  (2023-08-23T17:16:07Z)
• Two-Stage Neural Contextual Bandits for Personalised News Recommendation [50.3750507789989]
  Existing personalised news recommendation methods focus on exploiting user interests and ignores exploration in recommendation. We build on contextual bandits recommendation strategies which naturally address the exploitation-exploration trade-off. We use deep learning representations for users and news, and generalise the neural upper confidence bound (UCB) policies to generalised additive UCB and bilinear UCB.
  arXiv  Detail & Related papers  (2022-06-26T12:07:56Z)
• Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation [13.905580921329717]
  We propose a comprehensive framework to model user behaviors through positive feedback and negative feedback. The framework implicitly models the user using their session start time, and the article using its initial publishing time. Empirical evaluation on three real-world news datasets shows the framework's promising performance.
  arXiv  Detail & Related papers  (2022-05-12T12:47:06Z)
• Learning Robust Recommender from Noisy Implicit Feedback [140.7090392887355]
  We propose a new training strategy named Adaptive Denoising Training (ADT) ADT adaptively prunes the noisy interactions by two paradigms (i.e., Truncated Loss and Reweighted Loss) We consider extra feedback (e.g., rating) as auxiliary signal and propose three strategies to incorporate extra feedback into ADT.
  arXiv  Detail & Related papers  (2021-12-02T12:12:02Z)
• Existence conditions for hidden feedback loops in online recommender systems [0.0]
  We study how uncertainty and noise in user interests influence the existence of feedback loops. A non-zero probability of resetting user interests is sufficient to limit the feedback loop and estimate the size of the effect.
  arXiv  Detail & Related papers  (2021-09-11T13:30:08Z)
• Probabilistic and Variational Recommendation Denoising [56.879165033014026]
  Learning from implicit feedback is one of the most common cases in the application of recommender systems. We propose probabilistic and variational recommendation denoising for implicit feedback. We employ the proposed DPI and DVAE on four state-of-the-art recommendation models and conduct experiments on three datasets.
  arXiv  Detail & Related papers  (2021-05-20T08:59:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.