Context-Aware Recommender Systems

  • Gediminas Adomavicius University of Minnesota
  • Bamshad Mobasher DePaul University
  • Francesco Ricci Free University of Bozen-Bolzano
  • Alexander Tuzhilin New York University


Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

Author Biographies

Gediminas Adomavicius, University of Minnesota
Department of Information and Decision Sciences, Carlson School of Management
Bamshad Mobasher, DePaul University
School of Computing, College of Computing and Digital Media
Francesco Ricci, Free University of Bozen-Bolzano
Faculty of Computer Science
Alexander Tuzhilin, New York University
Stern School of Business
How to Cite
Adomavicius, G., Mobasher, B., Ricci, F., & Tuzhilin, A. (1). Context-Aware Recommender Systems. AI Magazine, 32(3), 67-80.