An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems

Xin Luo, Mengchu Zhou, Yunni Xia, Qingsheng Zhu

Research output: Contribution to journalArticlepeer-review

519 Scopus citations


Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.

Original languageEnglish (US)
Article number6748996
Pages (from-to)1273-1284
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Issue number2
StatePublished - May 2014

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Collaborative filtering (CF)
  • non-negative matrix-factorization (NMF)
  • recommender system
  • single-element-based approach
  • tikhonov regularization


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