A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method

Xin Luo, Meng Chu Zhou, Shuai Li, Zhuhong You, Yunni Xia, Qingsheng Zhu

Research output: Contribution to journalArticlepeer-review

190 Scopus citations

Abstract

Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matrix, which is critically important in collaborative filtering (CF)-based recommender systems. However, current NMF-based CF recommenders suffer from the problem of high computational and storage complexity, as well as slow convergence rate, which prevents them from industrial usage in context of big data. To address these issues, this paper proposes an alternating direction method (ADM)-based nonnegative latent factor (ANLF) model. The main idea is to implement the ADM-based optimization with regard to each single feature, to obtain high convergence rate as well as low complexity. Both computational and storage costs of ANLF are linear with the size of given data in the target matrix, which ensures high efficiency when dealing with extremely sparse matrices usually seen in CF problems. As demonstrated by the experiments on large, real data sets, ANLF also ensures fast convergence and high prediction accuracy, as well as the maintenance of nonnegativity constraints. Moreover, it is simple and easy to implement for real applications of learning systems.

Original languageEnglish (US)
Article number7112169
Pages (from-to)579-592
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume27
Issue number3
DOIs
StatePublished - Mar 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

Keywords

  • Alternating direction method
  • big data
  • collaborative filtering
  • recommender system
  • sparse matrices.

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