The continuous cold start problem in e-commerce recommender systems

L. Bernardi, J. Kamps, Y. Kiseleva, M.J.I. Mueller

    Research output: Book/ReportReportAcademic

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    Many e-commerce websites use recommender systems to recommend items to users. When a user or item is new, the system may fail because not enough information is available on this user or item. Various solutions to this `cold-start problem' have been proposed in the literature. However, many real-life e-commerce applications suffer from an aggravated, recurring version of cold-start even for known users or items, since many users visit the website rarely, change their interests over time, or exhibit different personas. This paper exposes the `Continuous Cold Start' (CoCoS) problem and its consequences for content- and context-based recommendation from the viewpoint of typical e-commerce applications, illustrated with examples from a major travel recommendation website,
    Original languageEnglish
    Number of pages6
    Publication statusPublished - 2015

    Publication series

    Volume1508.01177 [cs.IR]


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