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Fair Recommendations Through Diversity Promotion

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Book cover Advanced Data Mining and Applications (ADMA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10604))

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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.

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Notes

  1. 1.

    https://www.dropbox.com/sh/uwcp1xfz6afckif/AAAJiBeGUEhs0HEH4E-_-n-ca?d l=0.

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Author information

Authors and Affiliations

  1. Université de Lyon, UJM-Saint-Etienne, CNRS, Laboratoire Hubert Curien UMR 5516, 42023, Saint Etienne, France

    Pierre-René Lhérisson, Fabrice Muhlenbach & Pierre Maret

  2. 1D Lab, 5 rue Javelin Pagnon, 42000, Saint Etienne, France

    Pierre-René Lhérisson

Authors
  1. Pierre-René Lhérisson
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  2. Fabrice Muhlenbach
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  3. Pierre Maret
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Corresponding author

Correspondence to Pierre-René Lhérisson .

Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Gao Cong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  3. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

  4. Wuhan University, Wuhan, China

    Chengliang Li

  5. Nanyang Technological University, Singapore, Singapore

    Aixin Sun

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Lhérisson, PR., Muhlenbach, F., Maret, P. (2017). Fair Recommendations Through Diversity Promotion. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-69179-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69178-7

  • Online ISBN: 978-3-319-69179-4

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