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