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

2025/09/23 by Clarita Hawat, Hawat, Clarita, Wissam Al Jurdi +7
Computer Science · Engineering · #Data Stream Mining Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Traffic Prediction and Management Techniques

paper · pdf · doi:10.48550/arxiv.2509.18560

openalex publication_date 2025/09/23 · openalex created_date 2025/10/16 · openalex updated_date 2026/07/28

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.

Citations

• Group Validation in Recommender Systems: Framework for Multi-layer Performance Evaluation
• A systematic literature review on machine learning applications for\n consumer sentiment analysis using online reviews
• Extended Isolation Forest
• Automated Data Slicing for Model Validation:A Big data - AI Integration\n Approach
• Sequential Ensemble Learning for Outlier Detection: A Bias-Variance\n Perspective
• Semi-supervised Clustering Ensemble by Voting
• Exploiting Unlabeled Data to Enhance Ensemble Diversity
• Unsupervised Learning With Random Forest Predictors
• Greedy function approximation: A gradient boosting machine.
• Stacked generalization
• Evaluating collaborative filtering recommender systems

Related

• When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation
• Learning Robust Recommender from Noisy Implicit Feedback
• Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models
• Ensemble Boost: Greedy Selection for Superior Recommender Systems
• Large Language Model Enhanced Hard Sample Identification for Denoising Recommendation