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. (2011). Context-Aware Recommender Systems. AI Magazine, 32(3), 67-80.