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Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation

Author

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  • Kartik Hosanagar

    (Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Daniel Fleder

    (Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Dokyun Lee

    (Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Andreas Buja

    (Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer's preferences and recommend content best suited to him (e.g., “Customers who liked this also liked...”). A debate has emerged as to whether personalization has drawbacks. By making the Web hyperspecific to our interests, does it fragment Internet users, reducing shared experiences and narrowing media consumption? We study whether personalization is in fact fragmenting the online population. Surprisingly, it does not appear to do so in our study. Personalization appears to be a tool that helps users widen their interests, which in turn creates commonality with others. This increase in commonality occurs for two reasons, which we term volume and product-mix effects. The volume effect is that consumers simply consume more after personalized recommendations, increasing the chance of having more items in common. The product-mix effect is that, conditional on volume, consumers buy a more similar mix of products after recommendations. This paper was accepted by Sandra Slaughter, information systems.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormnsc:v:60:y:2014:i:4:p:805-823
    DOI: 10.1287/mnsc.2013.1808
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    24. Park, YoungSoo & Sim, Jeongeun & Kim, Bosung, 2022. "Online retail operations with “Try-Before-You-Buy”," European Journal of Operational Research, Elsevier, vol. 299(3), pages 987-1002.
    25. Chinchanachokchai, Sydney & Thontirawong, Pipat & Chinchanachokchai, Punjaporn, 2021. "A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).

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