A deep latent factor model for high-dimensional and sparse matrices in recommender systems

Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Recommender systems (RSs) commonly adopt a user-item rating matrix to describe users' preferences on items. With users and items exploding, such a matrix is usually high-dimensional and sparse (HiDS). Recently, the idea of deep learning has been applied to RSs. However, current deep-structured RSs suffer from high computational complexity. Enlightened by the idea of deep forest, this paper proposes a deep latent factor model (DLFM) for building a deep-structured RS on an HiDS matrix efficiently. Its main idea is to construct a deep-structured model by sequentially connecting multiple latent factor (LF) models instead of multilayered neural networks through a nonlinear activation function. Thus, the computational complexity grows linearly with its layer count, which is easy to resolve in practice. The experimental results on four HiDS matrices from industrial RSs demonstrate that when compared with state-of-the-art LF models and deep-structured RSs, DLFM can well balance the prediction accuracy and computational efficiency, which well fits the desire of industrial RSs for fast and right recommendations.

Original languageEnglish (US)
Article number8802269
Pages (from-to)4285-4296
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume51
Issue number7
DOIs
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Big data
  • deep model
  • high-dimensional and sparse (HiDS) matrix
  • latent factor (LF) analysis
  • recommender system (RS)

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