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Advances in Artificial IntelligenceVolume 2009 2009, Article ID 421425, 19 pages

Review ArticleDepartment of Computer Science and Engineering, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA

Received 9 February 2009; Accepted 3 August 2009

Academic Editor: Jun Hong

Copyright © 2009 Xiaoyuan Su and Taghi M. Khoshgoftaar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As one of the most successful approaches to building recommender systems, collaborative filtering CF uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms that combine CF with other recommendation techniques, with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.





Author: Xiaoyuan Su and Taghi M. Khoshgoftaar

Source: https://www.hindawi.com/



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