Matching Recommendation Technologies and Domains

  • Robin BurkeEmail author
  • Maryam Ramezani


Recommender systems form an extremely diverse body of technologies and approaches. The chapter aims to assist researchers and developers to identify the recommendation technologies that are most likely to be applicable to different domains of recommendation. Unlike other taxonomies of recommender systems, our approach is centered on the question of knowledge: what knowledge does a recommender system need in order to function, and where does that knowledge come from? Different recommendation domains (books vs condominiums, for example) provide different opportunities for the gathering and application of knowledge. These considerations give rise to a mapping between domain characteristics and recommendation technologies.


Content Knowledge Recommender System Social Knowledge Knowledge Source Item Feature 
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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This article is based on research performed by Ms. Ramezani at IBM Watson Research Center during the summer of 2007. An abbreviated version of the article with additional authors Lawrence Bergman, Rich Thompson and Bamshad Mobasher appeared as “Selecting and Applying Recommendation Technologies” at the Workshop on Recommendation and Collaboration at the Intelligent User Interfaces conference 2008.


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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Center for Web IntelligenceCollege of Computing and Digital Media, De-Paul UniversityChicagoUSA

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