Accelerating matrix factorization by overparameterization

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

This paper studies overparameterization on the matrix factorization (MF) model. We confirm that overparameterization can significantly accelerate the optimization of MF with no change in the expressiveness of the learning model. Consequently, modern applications on recommendations based on MF or its variants can largely benefit from our discovery. Specifically, we theoretically derive that applying the vanilla stochastic gradient descent (SGD) on the overparameterized MF model is equivalent to employing gradient descent with momentum and adaptive learning rate on the standard MF model. We empirically compare the overparameterized MF model with the standard MF model based on various optimizers, including vanilla SGD, AdaGrad, Adadelta, RMSprop, and Adam, using several public datasets. The experimental results comply with our analysis - overparameterization converges faster. The overparameterization technique can be applied to various learning-based recommendation models, including deep learning-based recommendation models, e.g., SVD++, nonnegative matrix factorization (NMF), factorization machine (FM), NeuralCF, Wide&Deep, and DeepFM. Therefore, we suggest utilizing the overparameterization technique to accelerate the training speed for the learning-based recommendation models whenever possible, especially when the size of the training dataset is large.

Original languageEnglish
Title of host publicationDeLTA 2020 - Proceedings of the 1st International Conference on Deep Learning Theory and Applications
EditorsAna Fred, Kurosh Madani
PublisherSciTePress
Pages89-97
Number of pages9
ISBN (Electronic)9789897584411
StatePublished - 2020
Event1st International Conference on Deep Learning Theory and Applications, DeLTA 2020 - Virtual, Online
Duration: 8 Jul 202010 Jul 2020

Publication series

NameDeLTA 2020 - Proceedings of the 1st International Conference on Deep Learning Theory and Applications

Conference

Conference1st International Conference on Deep Learning Theory and Applications, DeLTA 2020
CityVirtual, Online
Period8/07/2010/07/20

Keywords

  • Collaborative Filtering
  • Matrix Factorization
  • Overparameterization
  • Recommender Systems
  • SVD

Fingerprint

Dive into the research topics of 'Accelerating matrix factorization by overparameterization'. Together they form a unique fingerprint.

Cite this