缓解实际推系统中的混偏差,以部分无法访问的暴露状态
IEEE transactions on pattern analysis and machine intelligence
|October 25, 2023
概括
本研究为推系统 (RS) 引入了一种新的 debiasing 方法,以解决混偏差并提高准确性. 新方法有效地捕捉用户偏好,即使不完全的暴露数据,提高推的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 推系统 (RS) 对在线平台的用户体验至关重要,从用户反中学习.
- 现有的RS方法由于影响物品暴露和反的因素而遭受混偏差.
- 之前的退化策略难以同时捕获推特异性和暴露特异性知识,并处理噪音暴露数据.
研究的目的:
- 开发一种新的减肥推方法,以解决现有方法的局限性.
- 为了有效地同时捕捉推特定,暴露特定和常见知识.
- 为了实现对部分无法接触的暴露的稳定性,推系统产生了结果.
主要方法:
- 提出基于相互信息的反事实学习框架,利用特征,曝光和评级之间的因果关系.
- 显式模拟因果因素之间的关系,以捕捉推特定,暴露特定和常见知识.
- 采用双向学习策略,对部分无法访问的暴露数据进行稳定,并实施可优化的损失函数.
主要成果:
- 拟议的框架成功地捕捉了特定于建议的,特定于暴露的和共同的知识.
- 该方法证明了对部分无法获得的暴露结果的稳定性.
- 对公共数据集的广泛实验表明,在提高推准确性方面表现优越.
结论:
- 这种新的 debiasing 方法有效地减轻了推系统中的混偏差.
- 基于相互信息的反事实学习框架增强了捕捉微妙用户偏好的能力.
- 拟议的方法提供了一个强大的解决方案,以改善推的性能,即使有不完整的暴露信息.
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