Uncertainty-Calibrated Recommendations for Low-Active Users
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 recommendation for HAUs. To leverage this dichotomy, we introduce a unified, production-ready framework that calibrates uncertainty to drive differentiated strategies. ...
| Authors | Bob Junyi Zou, Sai Li, Tianyun Sun et al. |
|---|---|
| Primary category | cs.IR |
| Published | 18 May 2026 |
| Last updated at source | 25 May 2026 |
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