The Impact of a More Diversified Recommender System on Digital Platforms: Evidence from a Large-Scale Field Experiment

Abstract

Personalized recommender systems are widely used by major content platforms to boost user consumption and engagement by suggesting content that users have previously enjoyed and interacted with. A fundamental trade-off in designing these systems lies between exploitation and exploration: deciding whether to recommend familiar content that users favor or to introduce new, diverse content that may interest them in the future. We empirically examined this trade-off in a real-world-scale recommender system through a partnership with a leading global music-streaming service platform. We conducted a large-scale field experiment where users were randomly assigned to receive recommendations from either the platform’s standard algorithm or a modified version that recommends more diverse content. Contrary to industry expectations, increasing the diversity of the recommender algorithm does not enhance users’ consumption diversity; instead, it marginally reduces their click days on the platform. However, among active users—who account for most of the platform’s content usage—a 1% increase in recommendation diversity resulted in a 0.55% increase in their consumption diversity, without affecting overall consumption levels. The increase in consumption diversity corresponds with the more accurate prediction of their consumption preferences. The results suggest that the platform should tailor its algorithm to recommend more diverse content for active users.

Keywords: Recommender Systems, Social Media Platforms, Recommendation Diversity, Field Experiment