IDEAS home Printed from https://ideas.repec.org/p/cwl/cwldpp/1563.html
   My bibliography  Save this paper

Optimal Pricing with Recommender Systems

Author

Listed:

Abstract

We study optimal pricing in the presence of recommender systems. A recommender system affects the market in two ways: (i) it creates value by reducing product uncertainty for the customers and hence (ii) its recommendations can be offered as add-ons which generate informational externalities. The quality of the recommendation add-on is endogenously determined by sales. We investigate the impact of these factors on the optimal pricing by a seller with a recommender system against a competitive fringe without such a system. If the recommender system is sufficiently effective in reducing uncertainty, then the seller prices otherwise symmetric products differently to have some products experienced more aggressively. Moreover, the seller segments the market so that customers with more inflexible tastes pay higher prices to get better recommendations.

Suggested Citation

  • Dirk Bergemann & Deran Ozmen, 2006. "Optimal Pricing with Recommender Systems," Cowles Foundation Discussion Papers 1563, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:1563
    Note: CFP 1177
    as

    Download full text from publisher

    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d15/d1563.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paul Resnick & Christopher Avery & Richard Zeckhauser, 1999. "The Market for Evaluations," American Economic Review, American Economic Association, vol. 89(3), pages 564-584, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
    2. Sebastian Köhler & Thomas Wöhner & Ralf Peters, 2016. "The impact of consumer preferences on the accuracy of collaborative filtering recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(4), pages 369-379, November.
    3. Aridor, Guy & Gonçalves, Duarte, 2022. "Recommenders’ originals: The welfare effects of the dual role of platforms as producers and recommender systems," International Journal of Industrial Organization, Elsevier, vol. 83(C).
    4. Ian Ball & James Bono & Justin Grana & Nicole Immorlica & Brendan Lucier & Aleksandrs Slivkins, 2022. "Content Filtering with Inattentive Information Consumers," Papers 2205.14060, arXiv.org, revised Dec 2023.
    5. Lusi Li & Jianqing Chen & Srinivasan Raghunathan, 2018. "Recommender System Rethink: Implications for an Electronic Marketplace with Competing Manufacturers," Information Systems Research, INFORMS, vol. 29(4), pages 1003-1023, December.
    6. Anindya Ghose & Beibei Li & Siyuan Liu, 2019. "Mobile Targeting Using Customer Trajectory Patterns," Management Science, INFORMS, vol. 65(11), pages 5027-5049, November.
    7. Hong Jun Huang & Jun Yang & Benrong Zheng, 2021. "Demand effects of product similarity network in e-commerce platform," Electronic Commerce Research, Springer, vol. 21(2), pages 297-327, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Edgardo Arturo Ayala Gaytán, 2009. "Social network externalities and price dispersion in online markets," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-28, November.
    2. Gary E. Bolton & Elena Katok & Axel Ockenfels, 2004. "How Effective Are Electronic Reputation Mechanisms? An Experimental Investigation," Management Science, INFORMS, vol. 50(11), pages 1587-1602, November.
    3. Rockenbach, Bettina & Sadrieh, Abdolkarim, 2012. "Sharing information," Journal of Economic Behavior & Organization, Elsevier, vol. 81(2), pages 689-698.
    4. Britta Hoyer & Dirk van Straaten, 2021. "Anonymity and Self-Expression in Online Rating Systems - An Experimental Analysis," Working Papers Dissertations 70, Paderborn University, Faculty of Business Administration and Economics.
    5. Engström, Per & Forsell, Eskil, 2018. "Demand effects of consumers’ stated and revealed preferences," Journal of Economic Behavior & Organization, Elsevier, vol. 150(C), pages 43-61.
    6. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    7. Martin Abel & Rulof Burger & Patrizio Piraino, 2017. "The value of reference letters," Working Papers 06/2017, Stellenbosch University, Department of Economics.
    8. Juan Feng, 2004. "Optimal Allocation Mechanisms When Bidders Ranking for the objects is common," Econometric Society 2004 North American Summer Meetings 545, Econometric Society.
    9. Jonathan Levin, 2011. "The Economics of Internet Markets," Discussion Papers 10-018, Stanford Institute for Economic Policy Research.
    10. Paul Resnick & Richard Zeckhauser & John Swanson & Kate Lockwood, 2006. "The value of reputation on eBay: A controlled experiment," Experimental Economics, Springer;Economic Science Association, vol. 9(2), pages 79-101, June.
    11. Cristiano Codagnone & Federico Biagi & Fabienne Abadie, 2016. "The Passions and the Interests: Unpacking the ‘Sharing Economy’," JRC Research Reports JRC101279, Joint Research Centre.
    12. Gesche, Tobias, 2018. "Reference Price Shifts and Customer Antagonism: Evidence from Reviews for Online Auctions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181650, Verein für Socialpolitik / German Economic Association.
    13. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    14. Victor R. Fuchs, 2005. "Health, Government, and Irving Fisher," American Journal of Economics and Sociology, Wiley Blackwell, vol. 64(1), pages 407-425, January.
    15. Tobias J. Klein & Christian Lambertz & Konrad O. Stahl, 2016. "Market Transparency, Adverse Selection, and Moral Hazard," Journal of Political Economy, University of Chicago Press, vol. 124(6), pages 1677-1713.
    16. van Dolen, Willemijn & de Ruyter, Ko & Carman, James, 2006. "The role of self- and group-efficacy in moderated group chat," Journal of Economic Psychology, Elsevier, vol. 27(3), pages 324-343, June.
    17. Dellarocas, Chrysanthos, 2004. "The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    18. Lingfang (Ivy) Li & Erte Xiao, 2014. "Money Talks: Rebate Mechanisms in Reputation System Design," Management Science, INFORMS, vol. 60(8), pages 2054-2072, August.
    19. Hagiu, Andrei, 2009. "Why Do Intermediaries Divert Search?," Department of Economics, Working Paper Series qt3f34c5dk, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    20. Halliday, Simon D. & Lafky, Jonathan, 2019. "Reciprocity through ratings: An experimental study of bias in evaluations," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 83(C).

    More about this item

    Keywords

    Recommender system; Collaborative filtering; Add-ons; Pricing; Information externality;
    All these keywords.

    JEL classification:

    • D42 - Microeconomics - - Market Structure, Pricing, and Design - - - Monopoly
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cwl:cwldpp:1563. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Brittany Ladd (email available below). General contact details of provider: https://edirc.repec.org/data/cowleus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.