Uncertainty-Calibrated Recommendations for Low-Active Users
低活跃用户的不确定性校准推荐
机构 * Stanford University(斯坦福大学) ; ByteDance Inc.(字节跳动公司)
AI总结 提出一个生产就绪的框架,通过校准模型不确定性来为低活跃用户实施风险规避的去增强策略,为高活跃用户采用风险寻求的UCB策略,从而平衡推荐可靠性与多样性。
Comments Accepted to the Applied Data Science (ADS) track at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026)
Journal ref Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD '26), August 09--13, 2026, Jeju Island, Republic of Korea