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Similarity and Trust Metrics Used in Recommender Systems: A Survey

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

Recommender systems suggest the most appropriate items to users in order to help customers to find the most relevant items and facilitate sales. Collaborative filtering recommendation algorithm is the most successful technique for recommendation. In view of the fact that collaborative filtering systems depend on neighbors as the source of information, the recommendation quality of this approach depends on the neighbor’s selection. However, selecting neighbors can either stem from similarity or trust metrics. In this paper, we analyze these two types of neighbor’s selection metrics used in the field of recommendation in the literature. For each type, we first define it and then review different proposed metrics.

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Notes

  1. 1.

    http://www.journaldunet.com/ebusiness/le-net/1125265-nombre-d-utilisateurs-de-facebook-dans-le-monde/ (Juillet 2016).

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Authors and Affiliations

  1. MIRACL Laboratory, Technopole of Sfax, University of Sfax, P.O. Box 242, 3031, Sfax, Tunisia

    Maryam Jallouli, Sonia Lajmi & Ikram Amous

  2. Al Baha University, Al Bahah, Saudi Arabia

    Sonia Lajmi

Authors
  1. Maryam Jallouli
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  2. Sonia Lajmi
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  3. Ikram Amous
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Corresponding author

Correspondence to Maryam Jallouli .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Jallouli, M., Lajmi, S., Amous, I. (2017). Similarity and Trust Metrics Used in Recommender Systems: A Survey. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_102

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_102

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

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