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Multi-Criteria Recommender Systems

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Recommender Systems Handbook

Abstract

This chapter aims to provide an overview of the class of multi-criteria recommender systems, i.e., the category of recommender systems that use multi-criteria preference ratings. Traditionally, the vast majority of recommender systems literature has focused on providing recommendations by modelling a user’s utility (or preference) for an item as a single preference rating. However, where possible, capturing richer user preferences along several dimensions—for example, capturing not only the user’s overall preference for a given movie but also her preferences for specific movie aspects (such as acting, story, or visual effects)—can provide opportunities for further improvements in recommendation quality. As a result, a number of recommendation techniques that attempt to take advantage of such multi-criteria preference information have been developed in recent years. A review of current algorithms that use multi-criteria ratings for calculating predictions and generating recommendations is provided. The chapter concludes with a discussion on open issues and future challenges for the class of multi-criteria rating recommenders.

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Notes

  1. 1.

    In some recommender systems, R(u, i) might not contain the overall ratings r 0 in addition to k multi-criteria ratings, i.e., R(u, i) = (r 1, …, r k ). In this case, all the formulas in this subsection will still be applicable with index c ∈ { 1, …, k}, as opposed to c ∈ { 0, 1, …, k}.

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Authors and Affiliations

  1. Department of Information and Decision Sciences, University of Minnesota, 321 19th Avenue South, Minneapolis, MN, 55455, USA

    Gediminas Adomavicius

  2. Sookmyung Women’s University, Cheongpa-ro 47-gil 100, Yongsan-gu, Seoul, 140-742, Korea

    YoungOk Kwon

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  1. Gediminas Adomavicius
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Correspondence to Gediminas Adomavicius .

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  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Adomavicius, G., Kwon, Y. (2015). Multi-Criteria Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_25

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