通过精细的基于内容的行为检索来改善音乐推
IEEE transactions on cybernetics
|February 4, 2026
概括
音乐推模型与杂的听力历史斗争. 一个新的检索框架FactUBR使用内容和时间数据来选择代表性曲目,改进音乐建议.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息检索 信息检索
背景情况:
- 音乐流媒体平台产生了广泛的,通常是杂的,用户的听音历史.
- 由于这些杂的数据,通用顺序推模型在准确建模用户兴趣方面面临挑战.
- 现有的行为检索技术可能无法充分利用音乐内容和时间倾听模式的细微差别.
研究的目的:
- 介绍FactUBR,一个精细的,基于内容的,和时间的用户行为检索框架,用于音乐推.
- 解决通用顺序模型在处理长时间和杂的听力记录方面的局限性.
- 通过检索更好地反映用户偏好的代表性曲目来增强音乐推.
主要方法:
- FactUBR集成了一个基于强化内容的检索模块 (RCB) 和一个可微分时间通道 (DTC) 检索模块.
- RCB利用强化学习优化基于内容差异的检索决策,最大限度地提高多样性和推性能.
- DTC评估了收听段和候选曲目之间的微细相关性,使用扰乱最大化技术进行优化.
主要成果:
- 在增强各种顺序推模型时,FactUBR表现出显著的改进.
- 拟议的框架在实验中超过了现有的最先进的行为检索技术.
- 对两个公共音乐推基准进行了广泛的评估.
结论:
- 通过有效地检索代表性曲目,FactUBR提供了一个强大的解决方案来改进音乐建议.
- 该框架利用音乐内容和时间听力动态的能力对于增强性能至关重要.
- 实际上,FactUBR代表了对顺序推系统的用户行为检索的重大进步.
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