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International Conference on Digital Economy

ICDEc 2017: Digital Economy. Emerging Technologies and Business Innovation pp 208–217Cite as

Assessing Items Reliability for Collaborative Filtering Within the Belief Function Framework

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 290))

Abstract

Item-based collaborative filtering is among the most widely used recommendation approaches. It consists of identifying the most similar items in order to perform recommendations accordingly. However, the reliability of the information provided by these pieces of evidence cannot be fully trusted. Hence, quantifying their reliability seems imperative to form more valuable evidence. This paper contributes to the problem of covering uncertainty in the prediction process using the belief function theory. Our approach tends to take into account the different degrees of reliability of each similar item based on the discounting factor. Then, Dempster’s rule of combination is used as an aggregation operator to combine these pieces of evidence. The performance of the new evidential method is validated on a real world data set.

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Author information

Authors and Affiliations

  1. LARODEC, Institut Suprieur de Gestion de Tunis, Université de Tunis, Tunis, Tunisia

    Raoua Abdelkhalek, Imen Boukhris & Zied Elouedi

Authors
  1. Raoua Abdelkhalek
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  2. Imen Boukhris
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  3. Zied Elouedi
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Corresponding author

Correspondence to Raoua Abdelkhalek .

Editor information

Editors and Affiliations

  1. University of Manouba, Manouba, Tunisia

    Rim Jallouli

  2. University of Alberta, Edmonton, Alberta, Canada

    Osmar R. Zaïane

  3. University of Tunis, Tunis, Tunisia

    Mohamed Anis Bach Tobji

  4. University of Manouba, Manouba, Tunisia

    Rym Srarfi Tabbane

  5. University of Twente, Enschede, The Netherlands

    Anton Nijholt

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Abdelkhalek, R., Boukhris, I., Elouedi, Z. (2017). Assessing Items Reliability for Collaborative Filtering Within the Belief Function Framework. In: Jallouli, R., Zaïane, O., Bach Tobji, M., Srarfi Tabbane, R., Nijholt, A. (eds) Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2017. Lecture Notes in Business Information Processing, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-319-62737-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-62737-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62736-6

  • Online ISBN: 978-3-319-62737-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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