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Clustering-Based Recommender System: Bundle Recommendation Using Matrix Factorization to Single User and User Communities

  • Remigio Hurtado OrtizEmail author
  • Rodolfo Bojorque Chasi
  • César Inga Chalco
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 787)

Abstract

This paper shows the results of a Recommender System (RS) that suggests bundles of items to a user or a community of users. Nowadays, there are several RS that realize suggestions of a unique item considering the preferences of a user. However, these RS are not scalable and sometimes the suggestions that make are far from a user’s preferences. We propose an RS that suggests bundles of items to one user or a community of users with similar affinities. This RS uses an algorithm based on Matrix Factorization (MF). To execute the experiments, we use released databases with high dispersion. The results obtained are evaluated per the metrics Accuracy, Precision, Recall and F-measure. The results demonstrate that the proposed method improves significantly the quality of the suggestions.

Keywords

Recommender System Bundles of items Matrix Factorization 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Remigio Hurtado Ortiz
    • 1
    • 2
    Email author
  • Rodolfo Bojorque Chasi
    • 1
    • 2
  • César Inga Chalco
    • 1
  1. 1.Universidad Politécnica SalesianaCuencaEcuador
  2. 2.Universidad Politécnica de MadridMadridSpain

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