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Several real-life applications require crafting concise, quantitative scoringfunctions (also called rating systems) from measured observations. For example,an effectiveness score needs to be created for advertising campaigns using anumber of engagement metrics. Experts often need to create such scoringfunctions in the absence of labelled data, where the scores need to reflectbusiness insights and rules as understood by the domain experts. Without a wayto capture these inputs systematically, this becomes a time-consuming processinvolving trial and error. In this paper, we introduce a label-free practicalapproach to learn a scoring function from multi-dimensional numerical data. Theapproach incorporates insights and business rules from domain experts in theform of easily observable and specifiable constraints, which are used as weaksupervision by a machine learning model. We convert such constraints into lossfunctions that are optimized simultaneously while learning the scoringfunction. We examine the efficacy of the approach using a synthetic dataset aswell as four real-life datasets, and also compare how it performs vis-a-vissupervised learning models.

I Know Therefore I Score: Label-Free Crafting of Scoring Functions using Constraints Based on Domain Expertise