Consumer Choice between Recommendation Algorithms - Experimental Evidence

Felix Schleef ()
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Felix Schleef: HEC Paris - Ecole des Hautes Etudes Commerciales, CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique

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Abstract: Regulators are increasingly concerned with the power of large online platforms to bias consumer recommendations. In light of these concerns, I study experimentally whether giving consumers the choice between several recommendation algorithms can improve consumer welfare. I develop a novel experimental setting in which I first elicit the subjects' preferences and then expose them to choice tasks intermediated by personalized recommendation algorithms. When faced with a costly choice between two algorithms, subjects have a positive willingness-to-pay for better recommendations. However, subjects underestimate the potential gain from the better algorithm: a \$1 increase in estimated gain raises willingness-to-pay by only \$0.15 on average. These findings suggest that giving consumers power over recommendation algorithms to curtail potential abuse is not straightforward. However, it may be a viable business for platforms to offer improved recommendation algorithms to consumers for a fee.

Date: 2026-03-19
Note: View the original document on HAL open archive server: https://hal.science/hal-05559513v1
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