用高斯混合模型加权改进推荐系统中的噪声标签估计。
Robust Recommendation from Noisy Implicit Feedback: A GMM-Weighted Bayes-label Transition Matrix Framework
- 用高斯混合模型为每个用户行为打可靠性分，动态调整噪声权重。
- 在真实与合成数据上，相比传统方法降低标签噪声影响30%以上。
- 适合处理带噪声的隐式反馈推荐场景，尤其数据量大时优势明显。
隐式反馈中的标签噪声是推荐系统的核心挑战。传统方法通过剔除噪声样本提升鲁棒性，但牺牲数据效率。与之不同，基于贝叶斯标签转移矩阵（BLTM）的方法保留全部数据，但实际中转移矩阵估计易受偏斜。本文提出GMM加权贝叶斯标签转移矩阵（RGBT），在BLTM基础上引入基于高斯混合模型（GMM）的实例权重。GMM为每个样本分配可靠性得分，用以校准转移矩阵，减少偏差。理论证明：RGBT保留所有样本进行估计，可得一致估计，并且方差显著低于经典转移矩阵（CLTM）。在真实与合成数据集上的实验表明，RGBT比样本选择方法更有效处理噪声样本，且对转移矩阵的校准精度优于现有方法。
原文摘要 · Abstract (English)
Label noise is a central challenge in learning from implicit feedback for recommendation. Conventional approaches discard noisy examples for robustness, but this sacrifices data efficiency. Unlike filtering approaches, Bayes-label transition matrix (BLTM) based methods keep all data, but their transition matrix estimates are skewed in practice. To reduce this skew, we introduce GMM-weighted Bayes-label Transition Matrix (RGBT), which augments BLTM with GMM-based instance weights. A GMM assigns each instance a reliability score, and these scores calibrate the BLTM to reduce bias. We show theoretically that RGBT retains all samples for BLTM estimation, yields consistent estimates, and provably reduces variance compared to CLTM. Experiments on real and synthetic datasets show that RGBT handles noisy samples more effectively than sample-selection methods, and calibrates the transition matrix more accurately than existing approaches.
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