DeepAI AI Chat
Log In Sign Up

The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify

by   David Holtz, et al.

It remains unknown whether personalized recommendations increase or decrease the diversity of content people consume. We present results from a 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. Treatment users' recommendations were personalized based on their music listening history, whereas control users were recommended popular podcasts among users in their demographic group. We find that, on average, the treatment increased podcast streams by 28.90 decreased the average individual-level diversity of podcast streams by 11.51 and increased the aggregate diversity of podcast streams by 5.96 that personalized recommendations have the potential to create patterns of consumption that are homogenous within and diverse across users, a pattern reflecting Balkanization. Our results provide evidence of an "engagement-diversity trade-off" when recommendations are optimized solely to drive consumption: while personalized recommendations increase user engagement, they also affect the diversity of consumed content. This shift in consumption diversity can affect user retention and lifetime value, and impact the optimal strategy for content producers. We also observe evidence that our treatment affected streams from sections of Spotify's app not directly affected by the experiment, suggesting that exposure to personalized recommendations can affect the content that users consume organically. We believe these findings highlight the need for academics and practitioners to continue investing in personalization methods that explicitly take into account the diversity of content recommended.


page 13

page 15

page 16

page 17

page 31

page 32

page 33

page 34


OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers

To promote engagement, recommendation algorithms on platforms like YouTu...

The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens

We conduct a field experiment on a movie-recommendation platform to iden...

Follow the guides: disentangling human and algorithmic curation in online music consumption

The role of recommendation systems in the diversity of content consumpti...

No Video Left Behind: A Utility-Preserving Obfuscation Approach for YouTube Recommendations

Online content platforms optimize engagement by providing personalized r...

Influence of Selective Exposure to Viewing Contents Diversity

Personalization, including both self-selected and pre-selected, is inevi...

Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity

The Heterogeneous Information Network (HIN) formalism is very flexible a...

Designing a Novel Method for Personalizing Recommendations to Decrease Plastic Pollution

Third world countries tend to have a higher share of plastic waste that ...