Abstract
We present results from a large-scale, randomized field experiment on Spotify testing the effect of personalized recommendations on consumption diversity. In the experiment, both control and treatment users were given podcast recommendations, with the sole aim of increasing podcast consumption. However, the recommendations provided to treatment users were personalized based on their music listening history, whereas control users were recommended the most popular podcasts among users in their demographic group. Consistent with previous studies, we find that the treatment increased the average number of podcast streams per user. However, we also find the treatment decreased the average individual-level diversity of podcast streams and increased the aggregate diversity of podcast streams, indicating that personalized recommendations have the potential to create consumption patterns that are homogenous within users and diverse across users. Our results provide evidence of an "engagement-diversity trade-off" when optimizing solely for increased consumption: while personalized recommendations increase user engagement, they also affect the diversity of content that users consume. This shift in consumption diversity can affect user retention and lifetime value, and also impact the optimal strategy for content producers. Additional analyses suggest that exposure to personalized recommendations can also affect the content that users consume organically. We believe these findings highlight the need for both academics and practitioners to continue investing in approaches to personalization that explicitly take into account the diversity of content recommended to users.