用自适应集成学习提升推荐系统在噪声数据下的表现
When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation
- 通过稀疏门控网络动态选择专家模型进行去噪
- 在多数据集上优于主流方法，尤其在强噪声环境下
- 适合需要鲁棒推荐的工业场景和研究者参考
从隐式反馈中学习用户偏好是推荐系统的核心挑战，难点在于隐式反馈中存在潜在噪声。近年来，众多去噪推荐方法被提出，但大多过度依赖超参数配置，导致模型适应性和泛化能力不足。本文提出一种自适应集成学习（AEL）框架，采用稀疏门控网络作为核心，为不同数据样本选择合适专家，合成有效的去噪能力。为解决集成学习中模型复杂度高、子推荐器多样性不足的问题，提出通过堆叠组件构建子推荐器，而非直接构造。在多个数据集上的大量实验表明，AEL在多种主流指标上均表现优异，即使在存在大量且动态变化的噪声情况下仍保持领先。代码已开源：https://github.com/cpu9xx/AEL。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。