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Economics of Recommender Systems

Published: 08 October 2024 Publication History

Abstract

This tutorial dives into the economics of recommender systems (RSs), presenting existing and ongoing research on how they influence consumer choices, shape market outcomes, and change the incentives of those who interact with them, whether by designing, catering to, or using these systems. The tutorial also touches on the broader implications of this research for antitrust and competition policy. By fostering a collaborative and interdisciplinary research community, this tutorial aims to deepen the understanding of the economic effects of recommender systems and inform the development of policies to mitigate potential risks associated with their diffusion.

References

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Luis Aguiar and Joel Waldfogel. 2018. Platforms, promotion, and product discovery: Evidence from Spotify playlists. NBER Working Paper w24713. National Bureau of Economic Research.
[2]
Guy Aridor and Diogo Gonçalves. 2022. Recommenders’ Originals: The Welfare Effects of the Dual Role of Platforms as Producers and Recommender Systems. International Journal of Industrial Organization 83, 2 (2022), 215–235.
[3]
Mark Armstrong, John Vickers, and Jidong Zhou. 2009. Prominence and Consumer Search. Rand Journal of Economics 40, 2 (2009), 209–233.
[4]
Marc Bourreau and Germain Gaudin. 2022. Streaming Platform and Strategic Recommendation Bias. Journal of Economics & Management Strategy 31, 1 (2022), 25–47.
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Erik Brynjolfsson, Yu Jeffrey Hu, and Duncan Simester. 2011. Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales. Management Science 57, 8 (2011), 1373–1386.
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Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolo, and Salvatore Pastorello. 2023. Artificial intelligence, algorithmic recommendations and competition. Algorithmic Recommendations and Competition (May 14, 2023).
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Emilio Calvano and Bruno Jullien. 2018. Can We Trust the Algorithms That Recommend Products Online? A Theory of Biased Advice. Working Paper.
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Jacopo Castellini, Amelia Fletcher, Peter L. Ormosi, and Rahul Savani. 2023. Recommender Systems and Competition on Subscription-Based Platforms. Working paper.
[9]
Allison JB Chaney, Brandon M Stewart, and Bryan E Engelhardt. 2018. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. In Proceedings of the 12th ACM Conference on Recommender Systems. 224–232.
[10]
Yeon-Koo Che and Johannes Hörner. 2018. Recommender Systems as Mechanisms for Social Learning. The Quarterly Journal of Economics 133, 2 (2018), 871–925.
[11]
Nien-he Chen and Hong-Ting Tsai. 2023. Steering via Algorithmic Recommendations. RAND Journal of Economics (2023). Forthcoming.
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Alexandre De Cornière and Greg Taylor. 2019. A Model of Biased Intermediation. The RAND Journal of Economics 50, 4 (2019), 854–882.
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Daniel Fleder and Kartik Hosanagar. 2009. Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. Management Science 55, 5 (2009), 697–712.
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Amelia Fletcher, Peter L Ormosi, Rahul Savani, and Jacopo Castellini. 2023. Biased Recommender Systems and Supplier Competition. Working paper.
[15]
Giacomo Lee and Julian Wright. 2021. Recommender Systems and the Value of User Data. Working Paper.

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cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
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Published: 08 October 2024

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Author Tags

  1. Algorithmic Recommendations
  2. Machine Learning and Competition Policy
  3. Recommender Systems

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