Recommender Systems: An Overview

Recommender systems are tools for interacting with large and complex information spaces. 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. 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.