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Fair Recommendations Through Diversity Promotion

  • Pierre-René LhérissonEmail author
  • Fabrice Muhlenbach
  • Pierre Maret
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10604)

Abstract

We address the problem of overspecialization in streaming platform recommender systems. The personalization of web pages by delivering content to users is a challenging task in data mining. But it has been proved that beside optimizing the relevance accuracy such systems should also rely on other factors like diversity or novelty. In this paper we focus on modeling users’ boundary area of interest by selecting the most diverse items they liked in the past. We apply diversification while building the top-N list of recommendations. We select the items we want to recommend from an area where we consider a user will find item different from what she or he likes in the past. We evaluate our approach in offline analysis on two datasets, showing that our approach brings diversity and is competitive against implicit state-of-the-art method.

Keywords

Recommender systems Diversity Accuracy Content-based Multimedia streaming 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pierre-René Lhérisson
    • 1
    • 2
    Email author
  • Fabrice Muhlenbach
    • 1
  • Pierre Maret
    • 1
  1. 1.Université de Lyon, UJM-Saint-Etienne, CNRS, Laboratoire Hubert Curien UMR 5516Saint EtienneFrance
  2. 2.1D Lab, 5 rue Javelin PagnonSaint EtienneFrance

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