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International Workshop on Neural Networks

WIRN 2015: Advances in Neural Networks pp 87–96Cite as

A Rule Based Recommender System

Part of the Smart Innovation, Systems and Technologies book series (SIST,volume 54)

Abstract

We introduce a new recommending paradigm based on the genomic features of the candidate objects. The system is based on the tree structure of the object metadata which we convert in acceptance rules, leaving the user the discretion of selecting the most convincing rules for her/his scope. We framed the deriving recommendation system on a content management platform within the scope of the European Project NETT and tested it on the Entree UCI benchmark.

Keywords

  • Recommender system
  • Decision trees
  • Genomic features

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Notes

  1. 1.

    http://siren.laren.di.unimi.it/nett/mnett/.

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Acknowledgments

This work has been supported by the European Project NETT.

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Milano, Via Comelico 39/41, 20135, Milan, Italy

    Bruno Apolloni, Simone Bassis, Marco Mesiti, Stefano Valtolina & Francesco Epifania

  2. Social Things S.r.l., Via de Rolandi Battista n. 1, 20126, Milano, Italy

    Francesco Epifania

Authors
  1. Bruno Apolloni
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  2. Simone Bassis
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  3. Marco Mesiti
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  4. Stefano Valtolina
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  5. Francesco Epifania
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Corresponding author

Correspondence to Simone Bassis .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  3. Dept. of Info., Mathematics, Ele & Trans, Univ. Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Apolloni, B., Bassis, S., Mesiti, M., Valtolina, S., Epifania, F. (2016). A Rule Based Recommender System. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-33747-0_9

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