When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation

TL;DR

This paper introduces an Adaptive Ensemble Learning approach for denoising in recommendation systems, effectively handling noisy implicit feedback by dynamically selecting experts and stacking components to improve robustness and generalization.

Contribution

It proposes a novel AEL framework with a sparse gating network and component stacking, enhancing denoising capability and model diversity in recommendation systems.

Findings

AEL outperforms existing methods on various datasets.

The approach maintains robustness under substantial and dynamic noise.

It improves recommendation accuracy and generalization.

Abstract

Learning user preferences from implicit feedback is one of the core challenges in recommendation. The difficulty lies in the potential noise within implicit feedback. Therefore, various denoising recommendation methods have been proposed recently. However, most of them overly rely on the hyperparameter configurations, inevitably leading to inadequacies in model adaptability and generalization performance. In this study, we propose a novel Adaptive Ensemble Learning (AEL) for denoising recommendation, which employs a sparse gating network as a brain, selecting suitable experts to synthesize appropriate denoising capacities for different data samples. To address the ensemble learning shortcoming of model complexity and ensure sub-recommender diversity, we also proposed a novel method that stacks components to create sub-recommenders instead of directly constructing them. Extensive experiments across various datasets demonstrate that AEL outperforms others in kinds of popular metrics, even in the presence of substantial and dynamic noise. Our code is available at https://github.com/cpu9xx/AEL.