IDEAS home Printed from https://ideas.repec.org/p/net/wpaper/0844.html
   My bibliography  Save this paper

Recommender Systems and their Effects on Consumers: The Fragmentation Debate

Author

Abstract

Recommender systems are becoming integral to how consumers discover media. The value that recommenders offer is personalization: in environments with many product choices, recommenders personalize the browsing and consumption experience to each userÕs taste. Popular applications include product recommendations at e-commerce sites and online newspapers’ automated selection of articles to display based on the current reader’s interests. This ability to focus more closely on one's taste and filter all else out has spawned criticism that recommenders will fragment consumers. Critics say recommenders cause consumers to have less in common with one another and that the media should do more to increase exposure to a variety of content. Others, however, contend that recommenders do the opposite: they may homogenize users because they share information among those who would otherwise not communicate. These are opposing views, discussed in the literature for over ten years for which there is not yet empirical evidence. We present an empirical study of recommender systems in the music industry. In contrast to concerns that users are becoming more fragmented, we find that in our setting users’ purchases become more similar to one another. This increase in purchase similarity occurs for two reasons, which we term volume and taste effects. The volume effect is that consumers simply purchase more after recommendations, increasing the chance of having more purchases in common. The taste effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations. When we view consumers’ purchases as a similarity network before versus after recommendations, we find that the network becomes denser and smaller, or characterized by shorter inter-user distances. These findings suggest that for this setting, recommender systems are associated with an increase in commonality in consumption and that concerns of fragmentation may be misplaced.

Suggested Citation

  • Daniel Fleder & Kartik Hosanagar & Andreas Buja, 2008. "Recommender Systems and their Effects on Consumers: The Fragmentation Debate," Working Papers 08-44, NET Institute, revised Mar 2010.
  • Handle: RePEc:net:wpaper:0844
    as

    Download full text from publisher

    File URL: http://www.netinst.org/Fleder_Hosanagar_08-44.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joel Waldfogel & Lu Chen, 2006. "Does Information Undermine Brand? Information Intermediary Use And Preference For Branded Web Retailers," Journal of Industrial Economics, Wiley Blackwell, vol. 54(4), pages 425-449, December.
    2. Gal Oestreicher-Singer & Arun Sundararajan, 2009. "Recommendation Networks and the Long Tail of Electronic Commerce," Working Papers 09-03, NET Institute, revised Jan 2009.
    3. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    4. Greg Shaffer & Z. John Zhang, 1995. "Competitive Coupon Targeting," Marketing Science, INFORMS, vol. 14(4), pages 395-416.
    5. Marshall Van Alstyne & Erik Brynjolfsson, 2005. "Global Village or Cyber-Balkans? Modeling and Measuring the Integration of Electronic Communities," Management Science, INFORMS, vol. 51(6), pages 851-868, June.
    6. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    7. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    8. B. P. S. Murthi & Sumit Sarkar, 2003. "The Role of the Management Sciences in Research on Personalization," Management Science, INFORMS, vol. 49(10), pages 1344-1362, October.
    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. Srivastava, Abhishek & Bala, Pradip Kumar & Kumar, Bipul, 2020. "New perspectives on gray sheep behavior in E-commerce recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    2. Kartik Hosanagar & Daniel Fleder & Dokyun Lee & Andreas Buja, 2014. "Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation," Management Science, INFORMS, vol. 60(4), pages 805-823, April.
    3. Agam Gupta & Biswatosh Saha & Uttam K. Sarkar, 2017. "Emergent Heterogeneity in Keyword Valuation in Sponsored Search Markets: A Closer-to-Practice Perspective," Computational Economics, Springer;Society for Computational Economics, vol. 50(4), pages 687-710, December.
    4. 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. Kartik Hosanagar & Daniel Fleder & Dokyun Lee & Andreas Buja, 2014. "Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation," Management Science, INFORMS, vol. 60(4), pages 805-823, April.
    2. Yicheng Song & Nachiketa Sahoo & Elie Ofek, 2019. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation," Management Science, INFORMS, vol. 65(8), pages 3737-3757, August.
    3. Neeraj Arora & Xavier Dreze & Anindya Ghose & James Hess & Raghuram Iyengar & Bing Jing & Yogesh Joshi & V. Kumar & Nicholas Lurie & Scott Neslin & S. Sajeesh & Meng Su & Niladri Syam & Jacquelyn Thom, 2008. "Putting one-to-one marketing to work: Personalization, customization, and choice," Marketing Letters, Springer, vol. 19(3), pages 305-321, December.
    4. Fay, Scott & Mitra, Deb & Wang, Qiong, 2009. "Ask or infer? Strategic implications of alternative learning approaches in customization," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 136-152.
    5. Shuk Ying Ho & David Bodoff & Kar Yan Tam, 2011. "Timing of Adaptive Web Personalization and Its Effects on Online Consumer Behavior," Information Systems Research, INFORMS, vol. 22(3), pages 660-679, September.
    6. B. P. S. Murthi & Sumit Sarkar, 2003. "The Role of the Management Sciences in Research on Personalization," Management Science, INFORMS, vol. 49(10), pages 1344-1362, October.
    7. Walter W. Zhang & Sanjog Misra, 2022. "Coarse Personalization," Papers 2204.05793, arXiv.org, revised Aug 2024.
    8. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    9. Wayne Taylor & Brett Hollenbeck, 2021. "Leveraging loyalty programs using competitor based targeting," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 417-455, December.
    10. Ke-Wei Huang, 2009. "Optimal criteria for selecting price discrimination metrics when buyers have log-normally distributed willingness-to-pay," Quantitative Marketing and Economics (QME), Springer, vol. 7(3), pages 321-341, September.
    11. Yuxin Chen & Chakravarthi Narasimhan & Z. John Zhang, 2001. "Individual Marketing with Imperfect Targetability," Marketing Science, INFORMS, vol. 20(1), pages 23-41, November.
    12. Guy Aridor & Duarte Goncalves & Daniel Kluver & Ruoyan Kong & Joseph Konstan, 2022. "The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens," Papers 2211.14219, arXiv.org.
    13. Verhoef, Peter C. & Venkatesan, Rajkumar & McAlister, Leigh & Malthouse, Edward C. & Krafft, Manfred & Ganesan, Shankar, 2010. "CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions," Journal of Interactive Marketing, Elsevier, vol. 24(2), pages 121-137.
    14. Min Gao & Kecheng Liu & Zhongfu Wu, 2010. "Personalisation in web computing and informatics: Theories, techniques, applications, and future research," Information Systems Frontiers, Springer, vol. 12(5), pages 607-629, November.
    15. Gediminas Adomavicius & YoungOk Kwon, 2014. "Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 351-369, May.
    16. Felipe Thomaz & Carolina Salge & Elena Karahanna & John Hulland, 2020. "Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 43-63, January.
    17. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
    18. Juanjuan Zhang, 2011. "The Perils of Behavior-Based Personalization," Marketing Science, INFORMS, vol. 30(1), pages 170-186, 01-02.
    19. Knox, George & Datta, Hannes, 2020. "Streaming Services and the Homogenization of Music Consumption," Other publications TiSEM 0e4d6202-dcc5-4834-ba93-a, Tilburg University, School of Economics and Management.
    20. Huosong Xia & Xiang Wei & Wuyue An & Zuopeng Justin Zhang & Zelin Sun, 2021. "Design of electronic-commerce recommendation systems based on outlier mining," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 295-311, June.

    More about this item

    Keywords

    recommender systems; collaborative filtering; fragmentation; personalization; long tail;
    All these keywords.

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

    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:net:wpaper:0844. 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: Nicholas Economides (email available below). General contact details of provider: http://www.NETinst.org/ .

    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.