Context-aware recommender systems

Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, Alex Tuzhilin

Research output: Contribution to journalArticlepeer-review

249 Scopus citations

Abstract

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 intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in the 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.

Original languageEnglish (US)
Pages (from-to)67-80
Number of pages14
JournalAI Magazine
Volume32
Issue number3
DOIs
StatePublished - 2011

Fingerprint Dive into the research topics of 'Context-aware recommender systems'. Together they form a unique fingerprint.

Cite this