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Recommender Systems and their Effects on Consumers: The Fragmentation Debate

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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.

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  • 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
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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    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.

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    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

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