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Context-Aware Recommender Systems

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Recommender Systems Handbook

Abstract

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.

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Acknowledgements

Research of G. Adomavicius was supported in part by the National Science Foundation grant IIS-0546443, USA. The authors thank YoungOk Kwon for editorial assistance.

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Authors and Affiliations

  1. Department of Information and Decision Sciences Carlson School of Management, University of Minnesota, Waseca, Minnesota, USA

    Gediminas Adomavicius

  2. Department of Information, Operations and Management Sciences Stern School of Business, New York University, New York, NY, USA

    Alexander Tuzhilin

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  1. Gediminas Adomavicius
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  2. Alexander Tuzhilin
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Correspondence to Gediminas Adomavicius .

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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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Adomavicius, G., Tuzhilin, A. (2011). Context-Aware Recommender Systems. 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_7

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  • DOI: https://doi.org/10.1007/978-0-387-85820-3_7

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