Uncertainty in Repeated Implicit Feedback as a Measure of Reliability

2025-05-05

AI Summary

This paper addresses preference uncertainty arising from repeated implicit feedback (e.g., repeated plays) in music streaming. Unlike static assumptions, it systematically identifies interest drift and saturation effects in repeated interactions and proposes a Bayesian-based dynamic reliability modeling framework that explicitly quantifies interaction uncertainty as time-varying weights. The method integrates consistency assessment of implicit feedback, user interest evolution modeling, and uncertainty quantification, and introduces a reliability-weighted collaborative filtering mechanism. To enable reproducible research, the authors release the first publicly available dataset specifically designed for uncertainty analysis in repeated consumption scenarios. Extensive experiments on multiple benchmark recommendation tasks demonstrate significant improvements in recommendation accuracy and relevance, validating the substantial benefit of explicit uncertainty modeling for implicit-feedback recommender systems.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Recommender systems rely heavily on user feedback to learn effective user and item representations. Despite their widespread adoption, limited attention has been given to the uncertainty inherent in the feedback used to train these systems. Both implicit and explicit feedback are prone to noise due to the variability in human interactions, with implicit feedback being particularly challenging. In collaborative filtering, the reliability of interaction signals is critical, as these signals determine user and item similarities. Thus, deriving accurate confidence measures from implicit feedback is essential for ensuring the reliability of these signals. A common assumption in academia and industry is that repeated interactions indicate stronger user interest, increasing confidence in preference estimates. However, in domains such as music streaming, repeated consumption can shift user preferences over time due to factors like satiation and exposure. While literature on repeated consumption acknowledges these dynamics, they are often overlooked when deriving confidence scores for implicit feedback. This paper addresses this gap by focusing on music streaming, where repeated interactions are frequent and quantifiable. We analyze how repetition patterns intersect with key factors influencing user interest and develop methods to quantify the associated uncertainty. These uncertainty measures are then integrated as consistency metrics in a recommendation task. Our empirical results show that incorporating uncertainty into user preference models yields more accurate and relevant recommendations. Key contributions include a comprehensive analysis of uncertainty in repeated consumption patterns, the release of a novel dataset, and a Bayesian model for implicit listening feedback.

Problem

• Measuring uncertainty in repeated implicit feedback for reliability
• Addressing noise in implicit feedback for accurate recommendations
• Integrating uncertainty metrics to improve music streaming recommendations

Innovation

• Quantifies uncertainty in repeated implicit feedback patterns
• Integrates uncertainty as consistency metrics in recommendations
• Develops Bayesian model for implicit listening feedback