Learning Robust Recommender from Noisy Implicit Feedback
- URL: http://arxiv.org/abs/2112.01160v1
- Date: Thu, 2 Dec 2021 12:12:02 GMT
- Title: Learning Robust Recommender from Noisy Implicit Feedback
- Authors: Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua
- Abstract summary: 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.
- Score: 140.7090392887355
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ubiquity of implicit feedback makes it indispensable for building
recommender systems. However, it does not actually reflect the actual
satisfaction of users. For example, in E-commerce, a large portion of clicks do
not translate to purchases, and many purchases end up with negative reviews. As
such, it is of importance to account for the inevitable noises in implicit
feedback. However, little work on recommendation has taken the noisy nature of
implicit feedback into consideration. In this work, we explore the central
theme of denoising implicit feedback for recommender learning, including
training and inference. By observing the process of normal recommender
training, we find that noisy feedback typically has large loss values in the
early stages. Inspired by this observation, we propose a new training strategy
named Adaptive Denoising Training (ADT), which adaptively prunes the noisy
interactions by two paradigms (i.e., Truncated Loss and Reweighted Loss).
Furthermore, we consider extra feedback (e.g., rating) as auxiliary signal and
propose three strategies to incorporate extra feedback into ADT: finetuning,
warm-up training, and colliding inference. We instantiate the two paradigms on
the widely used binary cross-entropy loss and test them on three representative
recommender models. Extensive experiments on three benchmarks demonstrate that
ADT significantly improves the quality of recommendation over normal training
without using extra feedback. Besides, the proposed three strategies for using
extra feedback largely enhance the denoising ability of ADT.
Related papers
- CANDERE-COACH: Reinforcement Learning from Noisy Feedback [12.232688822099325]
The CANDERE-COACH algorithm is capable of learning from noisy feedback by a nonoptimal teacher.
We propose a noise-filtering mechanism to de-noise online feedback data, thereby enabling the RL agent to successfully learn with up to 40% of the teacher feedback being incorrect.
arXiv Detail & Related papers (2024-09-23T20:14:12Z) - Dual Test-time Training for Out-of-distribution Recommender System [91.15209066874694]
We propose a novel Dual Test-Time-Training framework for OOD Recommendation, termed DT3OR.
In DT3OR, we incorporate a model adaptation mechanism during the test-time phase to carefully update the recommendation model.
To the best of our knowledge, this paper is the first work to address OOD recommendation via a test-time-training strategy.
arXiv Detail & Related papers (2024-07-22T13:27:51Z) - Stability of Explainable Recommendation [10.186029242664931]
We study the vulnerability of existent feature-oriented explainable recommenders.
We observe that all the explainable models are vulnerable to increased noise levels.
Our study presents an empirical verification on the topic of robust explanations in recommender systems.
arXiv Detail & Related papers (2024-05-03T04:44:51Z) - Constructive Large Language Models Alignment with Diverse Feedback [76.9578950893839]
We introduce Constructive and Diverse Feedback (CDF) as a novel method to enhance large language models alignment.
We exploit critique feedback for easy problems, refinement feedback for medium problems, and preference feedback for hard problems.
By training our model with this diversified feedback, we achieve enhanced alignment performance while using less training data.
arXiv Detail & Related papers (2023-10-10T09:20:14Z) - Adapting Triplet Importance of Implicit Feedback for Personalized
Recommendation [43.85549591503592]
Implicit feedback is frequently used for developing personalized recommendation services.
We propose a novel training framework named Triplet Importance Learning (TIL), which adaptively learns the importance score of training triplets.
We show that our proposed method outperforms the best existing models by 3-21% in terms of Recall@k for the top-k recommendation.
arXiv Detail & Related papers (2022-08-02T19:44:47Z) - Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior.
We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference.
We show that CAFL improves recommendation quality when compared to prior correction methods.
arXiv Detail & Related papers (2022-07-04T17:58:39Z) - 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) - Self-Supervised Reinforcement Learning for Recommender Systems [77.38665506495553]
We propose self-supervised reinforcement learning for sequential recommendation tasks.
Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL.
Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC)
arXiv Detail & Related papers (2020-06-10T11:18:57Z)
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.