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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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Correspondence to María Emilia Charnelli .

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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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