Consumer Choice between Recommendation Algorithms - Experimental Evidence

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.