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Online serendipity: The case for curated recommender systems

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  • Kim, Henry M.
  • Ghiasi, Bita
  • Spear, Max
  • Laskowski, Marek
  • Li, Jiye

Abstract

When used effectively, recommender systems provide users with suggestions based on their own preferences. These systems first showed their value with e-commerce sites like Amazon and eBay, which provided recommendations algorithmically. A key drawback of these systems is that some items need personal touch recommendations to spur on purchase, use, or consumption. A recommender system that facilitates personal touch recommendations by enabling users to discover good recommenders as opposed to focusing on recommending items algorithmically addresses this drawback. In this article, we discuss such a system—a curated recommender system. A curated recommender system is optimal for online retailers and service providers, especially those that sell books, stream content, or provide social networking platforms.

Suggested Citation

  • Kim, Henry M. & Ghiasi, Bita & Spear, Max & Laskowski, Marek & Li, Jiye, 2017. "Online serendipity: The case for curated recommender systems," Business Horizons, Elsevier, vol. 60(5), pages 613-620.
    • Handle: RePEc:eee:bushor:v:60:y:2017:i:5:p:613-620
    DOI: 10.1016/j.bushor.2017.05.005
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    References listed on IDEAS

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    1. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
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    Cited by:

    1. Richa Kalpesh Saxena, 2024. "Toward an interface affordance model for online streaming video platforms," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 51(4), pages 469-485, December.
    2. , Aisdl, 2019. "Resources of The Serendipity Society," OSF Preprints sb9kp, Center for Open Science.

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