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A Taxonomy of Collaborative-Based Recommender Systems

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Web Personalization in Intelligent Environments

Part of the book series: Studies in Computational Intelligence ((SCI,volume 229))

Introduction

The explosive growth in the amount of information available in the WWW and the emergence of e-commerce in recent years has demanded new ways to deliver personalized content. Recommender systems [51] have emerged in this context as a solution based on collective intelligence to either predict whether a particular user will like a particular item or identify the collection of items that will be of interest to a certain user. Recommender systems have an excellent ability to characterize and recommend items within huge collections of data, what makes them a computerized alternative to human recommendations. Since useful personalized recommendations can add value to the user experience, some of the largest e-commerce web sites include recommender engines. Three well known examples are Amazon.com [1], LastFM [4] and Netflix [6].

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Authors
  1. Fabián P. Lousame
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  2. Eduardo Sánchez
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Editor information

Editors and Affiliations

  1. Computer Science Department, University of Bari, Via Orabona, 4, 70125, Bari, Italy

    Giovanna Castellano  & Anna Maria Fanelli  & 

  2. University of South Australia, Mawson Lakes Campus, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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Lousame, F.P., Sánchez, E. (2009). A Taxonomy of Collaborative-Based Recommender Systems. In: Castellano, G., Jain, L.C., Fanelli, A.M. (eds) Web Personalization in Intelligent Environments. Studies in Computational Intelligence, vol 229. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02794-9_5

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  • DOI: https://doi.org/10.1007/978-3-642-02794-9_5

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  • Online ISBN: 978-3-642-02794-9

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