Scalable Probabilistic Matrix Factorization with Graph-­Based Priors

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

Abstract

In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations learnt from the incomplete data matrix. We show that removing these contested edges improves prediction accuracy and scalability. We identify the contested edges through a highly-efficient graphical lasso approximation. The identification and removal of contested edges adds no computational complexity to state-of-the-art graph-regularized matrix factorization, remaining linear with respect to the number of non-zeros. Computational load even decreases proportional to the number of edges removed. Formulating a probabilistic generative model and using expectation maximization to extend graph-regularised alternating least squares (GRALS) guarantees convergence. Rich simulated experiments illustrate the desired properties of the resulting algorithm. On real data experiments we demonstrate improved prediction accuracy with fewer graph edges (empirical evidence that graph side-information is often inaccurate). A 300 thousand dimensional graph with three million edges (Yahoo music side-information) can be analyzed in under ten minutes on a standard laptop computer demonstrating the efficiency of our graph update.
Original languageEnglish
Title of host publicationProceedings of AAAI-20, AAAI Conference on Artificial Intelligence
PublisherAAAI
Pages5851-5858
Number of pages8
DOIs
Publication statusPublished - 3 Apr 2020
MoE publication typeA4 Article in a conference publication
EventAAAI Conference on Artificial Intelligence - New York, United States
Duration: 7 Feb 202012 Feb 2020
Conference number: 34
https://aaai.org/Conferences/AAAI-20/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number4
Volume34
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceAAAI Conference on Artificial Intelligence
Abbreviated titleAAAI
CountryUnited States
CityNew York
Period07/02/202012/02/2020
Internet address

Keywords

  • matrix factorization
  • scalability
  • graph side information

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