Inclusive Recommendations and User Engagement: Experimental Evidence from Pinterest

Madhav Kumar, Pedro Silva, Ashudeep Singh, Abhay Varmaraja

Abstract: We study the impact of diversifying recommendations for inclusivity on one of the largest visual content discovery platforms in the world, Pinterest. Pinterest re-designed its recommendation systems to improve the representation of all skin tones in recommended content and foster a more inclusive user experience. We describe the design of the new recommendation system and present results from a field experiment in which users across six countries were randomly assigned to receive a more diverse set of recommendations based on content skin tone. We find that the overall engagement rates remain stable and engagement with previously underrepresented content increases significantly. More broadly, users diversify their consumption by engaging with content from all skin tone ranges. We shed light on the mechanism driving these results using heterogeneous treatment effect analysis. We find that engagement for users with "preference for deeper skin tone content" increases significantly and engagement for users with "preference for lighter skin tone content" remains relatively stable. Finally, we analyze post-launch data to better understand the long-term implications of diversifying recommendations. Our research provides practical insights for platform managers and policymakers to create inclusive digital environments that promote engagement while catering to diverse user preferences.