arXiv:2605.17788v2 Announce Type: replace-cross Abstract: A fundamental challenge in recommender systems is balancing reliability for Low-Active Users (LAUs) with diversity for High-Active Users (HAUs). The key to this balance lies in quantifying model uncertainty, which approximates the risk of prediction errors and reveals the limits of the model's current knowledge. On large-scale short-video and livestream platforms, model uncertainty can warn of low-quality recommendations that may lead to disengagement of LAUs and at the same time identify opportunities to diversify content recommendatio
The paper addresses a long-standing challenge in recommender systems, particularly relevant as platforms grow and user engagement is crucial for business models.
Improving recommendation accuracy and user satisfaction for 'low-active users' can significantly increase platform stickiness and reach, impacting a broad range of digital services.
The explicit quantification and utilization of model uncertainty for tailoring recommendations introduces a more sophisticated approach to personalization, moving beyond simple engagement metrics.
- · Large-scale content platforms
- · AI/ML researchers in personalization
- · Low-active users
- · AI infrastructure providers
- · Platforms with unsophisticated recommendation engines
- · Basic collaborative filtering algorithms
Recommendations become more reliable and less prone to errors for users with limited interaction history.
Increased user retention and engagement, particularly among new or infrequent users, leading to broader user bases for platforms.
The methodology could extend to other AI applications where model uncertainty is critical for trustworthy decision-making, such as autonomous systems or medical diagnostics.
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Read at arXiv cs.LG