Recommender Systems: An Overview

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques e...

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Bibliographic Details
Published in:The AI magazine 2011-09, Vol.32 (3), p.13-18
Main Authors: Burke, Robin, Felfernig, Alexander, Göker, Mehmet H.
Format: Article
Language:English
Subjects:
Analysis
Artificial intelligence
Bookstores
Customization
Data mining
Data warehousing/data mining
E-commerce
Electronic commerce
Filtering systems
Information retrieval
Information sources
Information theory
Knowledge
Ontology
R&D
Recommendation systems
Recommender systems
Research & development
Search engines
Statistical methods
Studies
Utility functions
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Summary:Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case‐based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many online e‐commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of the articles in this special issue is to take stock of the current landscape of recommender systems research and identify directions the field is now taking. This article provides an overview of the current state of the field and introduces the various articles in the special issue.
ISSN:0738-4602
2371-9621
DOI:10.1609/aimag.v32i3.2361