The continuous cold start problem in e-commerce recommender systems

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

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    15 Citations (Scopus)
    3 Downloads (Pure)


    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, Keywords: Recommender systems, continous cold-start problem, industrial applications
    Original languageEnglish
    Title of host publication2nd Workshop on New Trends on Content-Based Recommender Systems (CBRecSys 2015, Vienna, Austria, September 20, 2015; co-located with RecSys 2015)
    EditorsT. Bogers, M. Koolen
    Publication statusPublished - 2015

    Publication series

    NameCEUR Workshop Proceedings
    ISSN (Print)1613-0073


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