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Recommender Systems: Sources of Knowledge and Evaluation Metrics

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Advanced Techniques in Web Intelligence-2

Part of the book series: Studies in Computational Intelligence ((SCI,volume 452))

Abstract

Recommender or Recommendation Systems (RS) aim to help users dealing with information overload: finding relevant items in a vast space of resources. Research on RS has been active since the development of the first recommender system in the early 1990s, Tapestry, and some articles and books that survey algorithms and application domains have been published recently. However, these surveys have not extensively covered the different types of information used in RS (sources of knowledge), and only a few of them have reviewed the different ways to assess the quality and performance of RS. In order to bridge this gap, in this chapter we present a classification of recommender systems, and then we focus on presenting the main sources of knowledge and evaluation metrics that have been described in the research literature.

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Authors and Affiliations

  1. School of Information Sciences, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA, 15260, USA

    Denis Parra

  2. Intelligent Systems Program, University of Pittsburgh, Sennott Square, Pittsburgh, PA, 15260, USA

    Shaghayegh Sahebi

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  1. Denis Parra
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  2. Shaghayegh Sahebi
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Correspondence to Denis Parra .

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  1. School of Engineering & Science, Dept. Industrial Engineering, University of Chile Republica, Santiago, Chile

    Juan D. Velásquez

  2. (OUCL), Oxford University Computing Laboratory, Parks Road, Oxford, OX1 3QD, United Kingdom

    Vasile Palade

  3. , School of Electrical and Information, University of South Australia, Mawson Lakes Campus, Adelaide, 5095, Australia

    Lakhmi C. Jain

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Parra, D., Sahebi, S. (2013). Recommender Systems: Sources of Knowledge and Evaluation Metrics. In: Velásquez, J., Palade, V., Jain, L. (eds) Advanced Techniques in Web Intelligence-2. Studies in Computational Intelligence, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33326-2_7

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