Recommender Systems: An Overview


  • Robin Burke DePaul University
  • Alexander Felfernig Graz University of Technology
  • Mehmet H. Göker Strands Labs, Inc.



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 on-line e-commerce applications such as, 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.




How to Cite

Burke, R., Felfernig, A., & Göker, M. H. (2011). Recommender Systems: An Overview. AI Magazine, 32(3), 13-18.