Comparative analysis of neighborhood-based approache and matrix factorization in recommender systems

Authors

  • Oleg Chertov National Technical University of Ukraine “Kyiv Polytechnic Institute” 37, Prospect Peremohy, Kyiv, Ukraine, 03056, Ukraine https://orcid.org/0000-0003-0087-1028
  • Armelle Brun University of Lorraine Campus scientifique, BP 239, Vandoeuvre-lès-Nancy Cedex, France, 54506, Ukraine
  • Anne Boyer University of Lorraine Campus scientifique, BP 239, Vandoeuvre-lès-Nancy Cedex, France, 54506, Ukraine https://orcid.org/0000-0001-5650-6295
  • Marharyta Aleksandrova National Technical University of Ukraine “Kyiv Polytechnic Institute” 37, Prospect Peremohy, Kyiv, Ukraine, 03056, Ukraine https://orcid.org/0000-0002-1863-0129

DOI:

https://doi.org/10.15587/1729-4061.2015.43074

Keywords:

collaborative filtering, neighborhood-based recommendations, matrix factorization-based recommendations, feature interpretation

Abstract

Unlike other works, this paper aims at searching a connection between two most popular approaches in recommender systems domain: Neighborhood-based (NB) and Matrix Factorization-based (MF). Provided analysis helps better understand advantages and disadvantages of each approach as well as their compatibility.

While NB relies on the ratings of similar users to estimate the rating of a user on an item, MF relies on the identification of latent features that represent the underlying relation between users and items. However, as it was shown in this paper, if latent features of Non-negative Matrix Factorization are interpreted as users, the processes of rating estimation by two methods become similar. In addition, it was shown through experiments that in this case elements of NB and MF are highly correlated. Still there is a major difference between Matrix Factorization-based and Neighborhood-based approaches: the first one exploits the same set of base elements to estimate unknown ratings (the set of latent features), while the second forms different sets of base elements (in this case neighbors) for each user-item pair.

Author Biographies

Oleg Chertov, National Technical University of Ukraine “Kyiv Polytechnic Institute” 37, Prospect Peremohy, Kyiv, Ukraine, 03056

Doctor of technical sciences, Head of the department

Applied Mathematics department

Armelle Brun, University of Lorraine Campus scientifique, BP 239, Vandoeuvre-lès-Nancy Cedex, France, 54506

PhD, Associate Professor

Lorraine Research Laboratory in Computer Science and its Applications (LORIA)

Anne Boyer, University of Lorraine Campus scientifique, BP 239, Vandoeuvre-lès-Nancy Cedex, France, 54506

PhD, Professor, Head of the KIWI research team

Lorraine Research Laboratory in Computer Science and its Applications (LORIA)

Marharyta Aleksandrova, National Technical University of Ukraine “Kyiv Polytechnic Institute” 37, Prospect Peremohy, Kyiv, Ukraine, 03056

PhD student

Applied Mathematics department

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Published

2015-06-29

How to Cite

Chertov, O., Brun, A., Boyer, A., & Aleksandrova, M. (2015). Comparative analysis of neighborhood-based approache and matrix factorization in recommender systems. Eastern-European Journal of Enterprise Technologies, 3(4(75), 4–9. https://doi.org/10.15587/1729-4061.2015.43074

Issue

Vol. 3 No. 4(75) (2015): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2015 Oleg Chertov, Armelle Brun, Anne Boyer, Marharyta Aleksandrova

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