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International Conference on Networked Systems

NETYS 2017: Networked Systems pp 101–108Cite as

A Distributed Recommender System Based on Graded Multi-label Classification

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Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10299))

Abstract

Recommender systems are designed to find items in which each user has most likely the highest interest. Items can be of any type such as commercial products, e-learning resources, movies, songs, and jokes. Successful web and mobile applications can collect easily thousands of users, thousands of items, and millions of item ratings in only few months. A solution to store and to process these continuously growing data is to build distributed recommender systems. The challenging task is to find the appropriate distribution strategy allowing an efficient retrieval of needed information. Considering the similarity between the task of predicting a rating, and the task of predicting a membership grade in graded multi-label classification (GMLC), we propose an adapted distribution strategy to efficiently build a decentralized recommender system based on GMLC.

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Notes

  1. 1.

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

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Author information

Authors and Affiliations

  1. Laboratoire Informatique de Mohammedia, FSTM, Hassan II University of Casablanca, BP 146, 20650, Mohammedia, Morocco

    Khalil Laghmari & Mohammed Ramdani

  2. Sorbonne Universités, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606, 4 Place Jussieu, 75005, Paris, France

    Khalil Laghmari & Christophe Marsala

Authors
  1. Khalil Laghmari
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  2. Christophe Marsala
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  3. Mohammed Ramdani
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Corresponding author

Correspondence to Khalil Laghmari .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, USA

    Amr El Abbadi

  2. Université de Lausanne, Lausanne, Switzerland

    Benoît Garbinato

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Laghmari, K., Marsala, C., Ramdani, M. (2017). A Distributed Recommender System Based on Graded Multi-label Classification. In: El Abbadi, A., Garbinato, B. (eds) Networked Systems. NETYS 2017. Lecture Notes in Computer Science(), vol 10299. Springer, Cham. https://doi.org/10.1007/978-3-319-59647-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-59647-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59646-4

  • Online ISBN: 978-3-319-59647-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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