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

Published: 13 July 2020 Publication History

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.

References

[1]
Ashton Anderson, Lucas Maystre, Ian Anderson, Rishabh Mehrotra, and Mounia Lalmas. 2020. Algorithmic effects on the diversity of consumption on spotify. In Proceedings of The Web Conference 2020. 2155--2165.
[2]
Sinan Aral and Paramveer Dhillon. 2016. Unpacking novelty: The anatomy of vision advantages. Available at SSRN 2388254 (2016).
[3]
Jörg Claussen, Christian Peukert, and Ananya Sen. 2019. The Editor vs. the Algorithm: Targeting, Data and Externalities in Online News. Data and Externalities in Online News (June 5, 2019) (2019).
[4]
Dokyun Lee and Kartik Hosanagar. 2019. How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment. Information Systems Research, Vol. 30, 1 (2019), 239--259.

Cited By

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  • (2024)“Fridays Are Racist”International Journal of Technoethics10.4018/IJT.34402515:1(1-19)Online publication date: 21-Jun-2024
  • (2024)Optimal Engagement-Diversity Tradeoffs in Social MediaProceedings of the ACM Web Conference 202410.1145/3589334.3645713(288-299)Online publication date: 13-May-2024
  • (2024)Playlist Continuation of Cold-Start Songs2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR62202.2024.00029(141-147)Online publication date: 7-Aug-2024
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  1. The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify

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        cover image ACM Conferences
        EC '20: Proceedings of the 21st ACM Conference on Economics and Computation
        July 2020
        937 pages
        ISBN:9781450379755
        DOI:10.1145/3391403
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        New York, NY, United States

        Publication History

        Published: 13 July 2020

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        Author Tags

        1. diversity
        2. field experiments
        3. recommender systems

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        EC '20
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        EC '20: The 21st ACM Conference on Economics and Computation
        July 13 - 17, 2020
        Virtual Event, Hungary

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        Overall Acceptance Rate 664 of 2,389 submissions, 28%

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        Cited By

        View all
        • (2024)“Fridays Are Racist”International Journal of Technoethics10.4018/IJT.34402515:1(1-19)Online publication date: 21-Jun-2024
        • (2024)Optimal Engagement-Diversity Tradeoffs in Social MediaProceedings of the ACM Web Conference 202410.1145/3589334.3645713(288-299)Online publication date: 13-May-2024
        • (2024)Playlist Continuation of Cold-Start Songs2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR62202.2024.00029(141-147)Online publication date: 7-Aug-2024
        • (2024)User Experiments on the Effect of the Diversity of Consumption on News ServicesIEEE Access10.1109/ACCESS.2024.336777012(31841-31852)Online publication date: 2024
        • (2023)Cookie consent has disparate impact on estimation accuracyProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667610(34308-34328)Online publication date: 10-Dec-2023
        • (2023)Selection Interface for Promoting User Selection Diversity by Presenting Positive/Negative Review Text and Video to Evoke Product Impression and User EmotionElectronics10.3390/electronics1212261112:12(2611)Online publication date: 9-Jun-2023
        • (2023)Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender SystemSSRN Electronic Journal10.2139/ssrn.4667778Online publication date: 2023
        • (2023)The Narrow-Taste Effect: When Consumers Display Narrow Tastes to Algorithmic RecommendersSSRN Electronic Journal10.2139/ssrn.4585195Online publication date: 2023
        • (2023)The Effects of Diversity in Algorithmic Recommendations on Digital Content Consumption: A Field ExperimentSSRN Electronic Journal10.2139/ssrn.4365121Online publication date: 2023
        • (2023)Digitization and the Market for Physical Works: Evidence from the Google Books ProjectAmerican Economic Journal: Economic Policy10.1257/pol.2021070215:4(428-458)Online publication date: 1-Nov-2023
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