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Argentine Congress of Computer Science

CACIC 2017: Computer Science – CACIC 2017 pp 179–187Cite as

Recommender System Based on Latent Topics

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Part of the Communications in Computer and Information Science book series (CCIS,volume 790)

Abstract

Collaborative filtering is one of the most used techniques in recommender systems. The goal of this paper is to propose a new method that uses latent topics to model the items to be recommended. In this way, the ability to establish a similarity between these elements is incorporated, improving the performance of the recommendation made. The performance of the proposed method has been measured in two very different contexts, yielding satisfactory results. Finally, the conclusions and some future lines of work are included.

Keywords

  • Recommender systems
  • Collaborative filtering
  • Latent topic modeling

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Author information

Authors and Affiliations

  1. LINTI - Research Laboratory in New Information Technologies, National University of La Plata, La Plata, Argentina

    María Emilia Charnelli & Javier Díaz

  2. III LIDI - Computer Science Research Institute LIDI Computer Science School, National University of La Plata, La Plata, Argentina

    Laura Lanzarini

  3. CONICET - National Scientific and Technical Research Council, Buenos Aires, Argentina

    María Emilia Charnelli

Authors
  1. María Emilia Charnelli
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  2. Laura Lanzarini
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  3. Javier Díaz
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Corresponding author

Correspondence to María Emilia Charnelli .

Editor information

Editors and Affiliations

  1. Universidad Nacional de La Plata, La Plata, Argentina

    Armando Eduardo De Giusti

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Charnelli, M.E., Lanzarini, L., Díaz, J. (2018). Recommender System Based on Latent Topics. In: De Giusti, A. (eds) Computer Science – CACIC 2017. CACIC 2017. Communications in Computer and Information Science, vol 790. Springer, Cham. https://doi.org/10.1007/978-3-319-75214-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-75214-3_17

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