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Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

  • Jesús SilvaEmail author
  • Noel Varela
  • Omar Bonerge Pineda Lezama
  • Hugo Hernández-P
  • Jairo Martínez Ventura
  • Boris de la Hoz
  • Leidy Pérez Coronel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

Abstract

The choice of academic itineraries and/or optional subjects to attend is not usually an easy decision since, in most cases, students lack the information, maturity, and knowledge required to make right decisions. This paper evaluates the support of Collaborative Systems for helping and guiding students in this decision-making process, considering the behavior and impact of these systems on the use of data different from the formal information the students usually use. For this purpose, the research applied the clustering based Multi-dimension Tensor Factorization approach to build a recommendation system and confirm that the increment in tensors improves the recommendation accuracy. As a result, this approach permits the user to take advantage of the contextual information to reduce the sparsity issue and increase the recommendation accuracy.

Keywords

Collaborative filtering Context aware recommendation system Contextual Modeling Item recommendations Multi-dimensionality Tensor Factorization 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jesús Silva
    • 1
    Email author
  • Noel Varela
    • 2
  • Omar Bonerge Pineda Lezama
    • 3
  • Hugo Hernández-P
    • 4
  • Jairo Martínez Ventura
    • 4
  • Boris de la Hoz
    • 4
  • Leidy Pérez Coronel
    • 4
  1. 1.Universidad Peruana de Ciencias AplicadasLimaPerú
  2. 2.Universidad de la CostaBarranquillaColombia
  3. 3.Universidad Tecnológica Centroamericana (UNITEC)San Pedro SulaHonduras
  4. 4.Corporación Universitaria LatinoamericanaBarranquillaColombia

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