Multi-Criteria Recommender Systems

  • Gediminas Adomavicius
  • Nikos Manouselis
  • YoungOk Kwon


This chapter aims to provide an overview of the class of multi-criteria recommender systems. First, it defines the recommendation problem as a multicriteria decision making (MCDM) problem, and reviews MCDM methods and techniques that can support the implementation of multi-criteria recommenders. Then, it focuses on the category of multi-criteria rating recommenders – techniques that provide recommendations by modelling a user’s utility for an item as a vector of ratings along several criteria. A review of current algorithms that use multi-criteria ratings for calculating predictions and generating recommendations is provided. Finally, the chapter concludes with a discussion on open issues and future challenges for the class of multi-criteria rating recommenders.


Data Envelopment Analysis Recommender System Aggregation Function Collaborative Filter Skyline Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Research of G. Adomavicius was supported in part by the National Science Foundation grant IIS-0546443, USA. Research of N. Manouselis was funded with support by the European Commission and more specifically, the project ECP-2006-EDU-410012 “Organic. Edunet: A Multilingual Federation of Learning Repositories with Quality Content for the Awareness and Education of European Youth about Organic Agriculture and Agroecology” of the eContentplus Programme.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Gediminas Adomavicius
    • 1
  • Nikos Manouselis
    • 2
  • YoungOk Kwon
    • 1
  1. 1.Department of Information and Decision Sciences, Carlson School of ManagementUniversity of MinnesotaMinneapolisUSA
  2. 2.Greek Research and Technology Network (GRNET S.A.)AthensGreece

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