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Building an Interpretable Recommender via Loss-Preserving Transformation by Dhurandhar, Amit; Oh, Sechan; Petrik, Marek is a scholarly article available to read on EtoBox.

What is Building an Interpretable Recommender via Loss-Preserving Transformation about?

We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification costs depend on both recommended actions and customers. Our method transforms such a data set to a new set which can be used with standard interpretable multi-class classification algorithms. The transformation has the desirable property that minimizing the standard misclassification penalty in this new space is equivalent to minimizing the custom cost function.

Author

Dhurandhar, Amit; Oh, Sechan; Petrik, Marek

Published

2016

Language

EN