Collaborative Filtering Based Recommendation Systems

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Author(s): E. Thirumaran (Indian Institute of Science, India)
Copyright: 2009
Pages: 16
Source title: Handbook of Research on Text and Web Mining Technologies
Source Author(s)/Editor(s): Min Song (New Jersey Institute of Technology, USA)and Yi-Fang Brook Wu (New Jersey Institute of Technology, USA)
DOI: 10.4018/978-1-59904-990-8.ch040

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

This chapter introduces Collaborative filtering-based recommendation systems, which has become an integral part of E-commerce applications, as can be observed in sites like Amazon.com. It will present several techniques that are reported in the literature to make useful recommendations, and study their limitations. The chapter also lists the issues that are currently open and the future directions that may be explored to address those issues. Furthermore, the authors hope that understanding of these limitations and issues will help build recommendation systems that are of high accuracy and have few false positive errors (which are products that are recommended, though the user does not like them).

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