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Recommender System Based on Latent Topics

  • María Emilia Charnelli
  • Laura Lanzarini
  • Javier Díaz
Conference paper
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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LINTI - Research Laboratory in New Information TechnologiesNational University of La PlataLa PlataArgentina
  2. 2.III LIDI - Computer Science Research Institute LIDI Computer Science SchoolNational University of La PlataLa PlataArgentina
  3. 3.CONICET - National Scientific and Technical Research CouncilBuenos AiresArgentina

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