LLORMA: Local low-rank matrix approximation

Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, Samy Bengio

Research output: Contribution to journalArticle

42 Scopus citations

Abstract

Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is low-rank. In this paper, we propose, analyze, and experiment with two procedures, one parallel and the other global, for constructing local matrix approximations. The two approaches approximate the observed matrix as a weighted sum of low-rank matrices. These matrices are limited to a local region of the observed matrix. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks.

Original languageEnglish (US)
JournalJournal of Machine Learning Research
Volume17
StatePublished - Mar 1 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

Keywords

  • Collaborative filtering
  • Kernel smoothing
  • Matrix approximation
  • Non-parametric methods
  • Recommender systems

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  • Cite this

    Lee, J., Kim, S., Lebanon, G., Singer, Y., & Bengio, S. (2016). LLORMA: Local low-rank matrix approximation. Journal of Machine Learning Research, 17.