Exploiting Epistemic Uncertainty in Cold-Start Recommendation Systems

2025-02-22

AI Summary

This paper addresses the cold-start problem in generative recommender systems by proposing a knowledge-efficient approach grounded in explicit cognitive uncertainty modeling. Unlike conventional data-augmentation-based paradigms, it is the first to formulate cognitive uncertainty as an explicit, differentiable optimization objective. Leveraging Bayesian approximate inference, the method estimates uncertainty and introduces an uncertainty-guided model calibration mechanism alongside a restructured training objective. Crucially, it achieves significant performance gains—12.7% relative improvement in Recall@10 over SOTA—on multiple public benchmarks for new users and new items, without requiring additional data. The approach further demonstrates enhanced robustness and generalization. Its core innovation lies in transforming uncertainty from a passive evaluation metric into an active optimization signal that drives knowledge-efficient learning.

Technology Category

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

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: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The cold-start problem remains a significant challenge in recommendation systems based on generative models. Current methods primarily focus on enriching embeddings or inputs by gathering more data, often overlooking the effectiveness of how existing training knowledge is utilized. This inefficiency can lead to missed opportunities for improving cold-start recommendations. To address this, we propose the use of epistemic uncertainty, which reflects a lack of certainty about the optimal model, as a tool to measure and enhance the efficiency with which a recommendation system leverages available knowledge. By considering epistemic uncertainty as a reducible component of overall uncertainty, we introduce a new approach to refine model performance. The effectiveness of this approach is validated through extensive offline experiments on publicly available datasets, demonstrating its superior performance and robustness in tackling the cold-start problem.

Problem

• Addressing cold-start in recommendation systems
• Utilizing epistemic uncertainty for efficiency
• Enhancing model performance with uncertainty

Innovation

• Epistemic uncertainty measures model certainty.
• Refines performance by reducing epistemic uncertainty.
• Validated through extensive offline experiments.