An incremental-and-static-combined scheme for matrix-factorization-based collaborative filtering

X. Luo, M. Zhou, H.K.N. Leung, Y. Xia, Q. Zhu, Z. You, Shuai Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Collaborative filtering (CF)-based recommenders are achieved by matrix factorization (MF) to obtain high prediction accuracy and scalability. Most current MF-based models, however, are static ones that cannot adapt to incremental user feedbacks. This work aims to develop a general, incremental- and-static-combined scheme for MF-based CF to obtain highly accurate and computationally affordable incremental recommenders. With it, a recommender is designed to consist of two components, i.e., a static one built on static rating data, and an incremental one built on a sub-matrix related to rating-variations only. Highly reliable predictions are thus generated by fusing their results. The experiments on large industrial datasets show that desired accuracy and acceptable computational complexity are achieved by the resulting recommender with the proposed scheme.
Original languageEnglish
Pages (from-to)333-343
Number of pages11
JournalIEEE Transactions on Automation Science and Engineering
Volume13
Issue number1
DOIs
Publication statusPublished - 2014

Keywords

  • Static model
  • Collaborative filtering
  • Incremental model
  • Matrix factorization
  • Recommender system
  • Scheme

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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