我知故我评：基于领域专业知识约束的无标签评分函数构建
I Know Therefore I Score: Label-Free Crafting of Scoring Functions using Constraints Based on Domain Expertise
AI总结：
本文提出一种无标签方法，利用领域专家约束作为弱监督，通过同步优化损失函数从多维数据学习评分函数，并在合成及四个真实数据集上验证其有效性。
摘要：
若干现实应用需要根据测量观测结果构建简洁、定量的评分函数（也称为评级系统）。例如，需要使用多个参与度指标为广告活动创建有效性评分。在没有标注数据的情况下，专家通常需要创建此类评分函数，其中分数需要反映领域专家所理解的业务洞察和规则。由于缺乏系统性地捕获这些输入的方法，这一过程变得耗时且涉及反复试验。在本文中，我们介绍了一种无标签的实用方法，用于从多维数值数据中学习评分函数。该方法以易于观察和可指定的约束形式整合领域专家的洞察和业务规则，这些约束被机器学习模型用作弱监督。我们将此类约束转换为损失函数，在学习评分函数的同时进行同步优化。我们使用合成数据集以及四个真实数据集检验了该方法的有效性，并比较了其相对于监督学习模型的性能表现。
英文摘要：
Several real-life applications require crafting concise, quantitative scoring functions (also called rating systems) from measured observations. For example, an effectiveness score needs to be created for advertising campaigns using a number of engagement metrics. Experts often need to create such scoring functions in the absence of labelled data, where the scores need to reflect business insights and rules as understood by the domain experts. Without a way to capture these inputs systematically, this becomes a time-consuming process involving trial and error. In this paper, we introduce a label-free practical approach to learn a scoring function from multi-dimensional numerical data. The approach incorporates insights and business rules from domain experts in the form of easily observable and specifiable constraints, which are used as weak supervision by a machine learning model. We convert such constraints into loss functions that are optimized simultaneously while learning the scoring function. We examine the efficacy of the approach using a synthetic dataset as well as four real-life datasets, and also compare how it performs vis-a-vis supervised learning models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
