When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation
This work addresses noise in recommendation systems for users and platforms, but it appears incremental as it builds on existing denoising methods with a novel ensemble approach.
The paper tackles the problem of learning user preferences from noisy implicit feedback in recommendation systems by proposing an Adaptive Ensemble Learning (AEL) method that uses a sparse gating network to select experts for denoising, and it demonstrates superior performance across various datasets in popular metrics.
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.