通过集成学习过滤推荐系统中的自然噪声，提升推荐准确性。
Understand your Users, An Ensemble Learning Framework for Natural Noise Filtering in Recommender Systems
- 构建三层次框架，融合多种噪声识别算法与集成模型。
- 量化评估意外发现（serendipity）并验证群体行为一致性。
- 适合关注推荐系统数据质量与用户满意度的研究者。
网络内容的爆炸式增长是推荐系统成功的关键。本文针对噪声定义难题展开研究，指出噪声与人类偏好和行为的变异密切相关。在识别用户倾向变化时，区分三类现象：直接影响用户情感的外部因素、引发意外偏好的偶然性发现（serendipity），以及被误认为噪声的偶然交互。为应对这些问题，提出一种新框架以识别噪声评分。该框架模块化设计，包含三层：用于物品分类的已知自然噪声算法、用于物品精炼评估的集成学习模型，以及基于特征签名的噪声识别机制。进一步提出可量化评估意外发现并进行群体验证的指标，显著提升推荐准确性的鲁棒性。本方法旨在生成更干净的训练数据，从而提升用户满意度与参与度。
原文摘要 · Abstract (English)
The exponential growth of web content is a major key to the success for Recommender Systems. This paper addresses the challenge of defining noise, which is inherently related to variability in human preferences and behaviors. In classifying changes in user tendencies, we distinguish three kinds of phenomena: external factors that directly influence users' sentiment, serendipity causing unexpected preference, and incidental interaction perceived as noise. To overcome these problems, we present a new framework that identifies noisy ratings. In this context, the proposed framework is modular, consisting of three layers: known natural noise algorithms for item classification, an Ensemble learning model for refined evaluation of the items and signature-based noise identification. We further advocate the metrics that quantitatively assess serendipity and group validation, offering higher robustness in recommendation accuracy. Our approach aims to provide a cleaner training dataset that would inherently improve user satisfaction and engagement with Recommender Systems.
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