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Context-Aware Recommender Systems

  • Gediminas Adomavicius
  • Alexander Tuzhilin
Chapter

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.

Keywords

Contextual Information Recommender System Contextual Modeling Recommendation Algorithm Movie Theater 
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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Notes

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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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Information and Decision Sciences Carlson School of ManagementUniversity of MinnesotaWasecaUSA
  2. 2.Department of Information, Operations and Management Sciences Stern School of BusinessNew York UniversityNew YorkUSA

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