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International Conference on Flexible Query Answering Systems

FQAS 2017: Flexible Query Answering Systems pp 71–83Cite as

A Typicality-Based Recommendation Approach Leveraging Demographic Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10333))

Abstract

In this paper, we introduce a new recommendation approach leveraging demographic data. Items are associated with the audience who liked them, and we consider similarity based on audiences. More precisely, recommendations are computed on the basis of the (fuzzy) typical demographic properties (age, sex, occupation, etc.) of the audience associated with every item. Experiments on the MovieLens dataset show that our approach can find predictions that other tested state-of-the-art systems cannot.

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Notes

  1. 1.

    http://grouplens.org/datasets/movielens/.

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Acknowledgments

This work has been partially funded by the French DGE (Direction Générale des Entreprises) under the project ODIN (Open Data INtelligence).

Author information

Authors and Affiliations

  1. Irisa – University of Rennes 1, Technopole Anticipa, 22305, Lannion Cedex, France

    Aurélien Moreau, Olivier Pivert & Grégory Smits

Authors
  1. Aurélien Moreau
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  2. Olivier Pivert
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  3. Grégory Smits
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Corresponding author

Correspondence to Aurélien Moreau .

Editor information

Editors and Affiliations

  1. Roskilde University, Roskilde, Denmark

    Henning Christiansen

  2. ENSSAT-IRISA, Lannion Cedex, France

    Hélène Jaudoin

  3. University of Westminster, London, United Kingdom

    Panagiotis Chountas

  4. Roskilde University, Roskilde, Denmark

    Troels Andreasen

  5. Aalborg University, Esbjerg, Denmark

    Henrik Legind Larsen

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Moreau, A., Pivert, O., Smits, G. (2017). A Typicality-Based Recommendation Approach Leveraging Demographic Data. In: Christiansen, H., Jaudoin, H., Chountas, P., Andreasen, T., Legind Larsen, H. (eds) Flexible Query Answering Systems. FQAS 2017. Lecture Notes in Computer Science(), vol 10333. Springer, Cham. https://doi.org/10.1007/978-3-319-59692-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-59692-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59691-4

  • Online ISBN: 978-3-319-59692-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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