Understand your Users, An Ensemble Learning Framework for Natural Noise Filtering in Recommender Systems

2025-09-22

AI Summary

In recommender systems, user behavioral data is inherently contaminated by natural noise arising from external events, serendipitous preferences, and accidental interactions—noise that existing methods struggle to identify and filter effectively. To address this, we propose a noise-aware ensemble learning framework specifically designed for modeling natural noise. First, we decouple temporal shifts in user preferences into three identifiable noise sources. Second, we introduce a modular three-layer architecture integrating behavior-signature-based noise detection with multi-granularity evaluation modules, jointly quantifying both serendipity and group-level recommendation effectiveness via dedicated metrics. Third, we perform end-to-end training to enable noise-aware data purification. Extensive experiments demonstrate that our approach significantly improves recommendation accuracy (+3.2% NDCG@10) and robustness, while enhancing adaptability to dynamic user behaviors.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

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.

Problem

• Defining noise related to human preference variability in recommender systems
• Identifying noisy ratings caused by external factors and incidental interactions
• Providing cleaner training data to improve recommendation accuracy and user satisfaction

Innovation

• Ensemble learning model for refined item evaluation
• Modular framework with three-layer noise identification
• Signature-based noise identification for rating filtering