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Recommender Systems Handbook pp 367–386Cite as

Matching Recommendation Technologies and Domains

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Abstract

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.

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Acknowledgements

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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  1. Center for Web Intelligence, College of Computing and Digital Media, De-Paul University, 243 S. Wabash Ave., Chicago, Illinois, USA

    Robin Burke & Maryam Ramezani

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  1. Robin Burke
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  2. Maryam Ramezani
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Correspondence to Robin Burke .

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Editors and Affiliations

  1. , Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, 39100, Italy

    Francesco Ricci

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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

    Bracha Shapira

  4. School of Communication,, Information & Library Studies, Rutgers University, Huntington Street 4, New Brunswick, 08901-1071, New Jersey, USA

    Paul B. Kantor

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Burke, R., Ramezani, M. (2011). Matching Recommendation Technologies and Domains. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_11

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