Mathematical  Culture and Thought

Mathematical Culture and Thought

Applications of Matrix Factorization in Recommender Systems

Document Type : Survey

Authors
Department of Mathematics, University of Qom, Iran
Abstract
Due to the huge amount of information available online, it seems necessary
to have a recommender system that automatically and intelligently suggests to users.
Recommendation systems constitute a specific type of information filtering technique
that attempt to present items according to the interest expressed by a user. Collaborative
Filtering (CF) is the process of evaluating information using the opinion of other
people and computes recommendations based on the information about similar items
or users. One of the challenging problems is the sparsity problem of the user-item
matrix. Collaborative recommenders try to capture relationships among user-user or
item-item pairs by reducing the dimensionality of the user or rather item space. Matrix
factorization techniques are employed to reduce the dimension of the observed
dataset. In our work we want to investigate the well-known Singular Value Decomposition
(SVD) and Semi Discrete Decomposition (SDD), which factorize a matrix into
three low-dimensional matrices. The comparison of the methods shows that although
SVD has the better results than SDD, SDD is efficient in terms of time and especially
memory by producing an error close to SVD.
Keywords

Subjects


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Volume 41, Issue 1 - Serial Number 70
November 2022
Pages 97-112

  • Receive Date 16 February 2019
  • Revise Date 10 November 2019
  • Accept Date 23 November 2019
  • Publish Date 23 October 2022