Integrating Choice and Predictive Theories: A Comparative Study of Set Generation and Machine Learning in Consideration Set Modeling

A comparative framework designed to evaluate the predictive capabilities of two distinct modeling approaches: traditional consideration and choice set generation models, and Machine Learning (ML) models showed that certain ML models can achieve over 80% accuracy in predicting individual considered items and over 60% accuracy in predicting entire consideration sets, across both population scenarios.