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

Published: 07 July 2023 Publication History

Abstract

We conduct a 6 month field experiment on a movie-recommendation platform to identify if and how recommendation systems affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We have three experimental groups: (a) control, (b) exposed, and (c) recommended + exposed goods where only goods in (c) are recommended and we elicit beliefs about goods in (b) and (c). Comparing across these treatment arms we find recommendations increase consumption beyond its role in exposing goods to consumers. We provide support for an informational mechanism: recommendations affect consumers' beliefs, which in turn explain consumption. Recommendations reduce uncertainty about goods consumers are most uncertain about and induce information acquisition. Finally, we find evidence for spatial correlation in beliefs.

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  • (2024)The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688158(1-1)Online publication date: 8-Oct-2024
  • (2024)Enhancing Movie Recommendations: A Deep Neural Network Approach with MovieLens Case Study2024 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC61514.2024.10592336(1303-1308)Online publication date: 27-May-2024

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cover image ACM Conferences
EC '23: Proceedings of the 24th ACM Conference on Economics and Computation
July 2023
1253 pages
ISBN:9798400701047
DOI:10.1145/3580507
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(s).

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2023

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Author Tags

  1. recommender systems
  2. information acquisition
  3. field experiment

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  • Extended-abstract

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EC '23
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EC '23: 24th ACM Conference on Economics and Computation
July 9 - 12, 2023
London, United Kingdom

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

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Cited By

View all
  • (2024)The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688158(1-1)Online publication date: 8-Oct-2024
  • (2024)Enhancing Movie Recommendations: A Deep Neural Network Approach with MovieLens Case Study2024 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC61514.2024.10592336(1303-1308)Online publication date: 27-May-2024

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