Work

Context‐Aware Recommender Systems

Year: 2011

Type: article

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.

Source: AI Magazine

DOI: 10.1609/aimag.v32i3.2364

Authors: Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, Alex Tuzhilin

Institutions: University of Minnesota, DePaul University, Free University of Bozen-Bolzano, New York University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Award: CAREER: Next Generation Personalization Technologies