An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems

Xin Luo, Mengchu Zhou, Shuai Li, Yunni Xia, Zhuhong You, Qingsheng Zhu, Hareton Leung

Research output: Contribution to journalArticlepeer-review

99 Scopus citations


Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to some desired latent features; however, most of them employ first-order optimization algorithms, e.g., gradient decent schemes, to conduct their optimization task, thereby failing in discovering patterns reflected by higher order information. This work proposes to build a new LF-based CF model via second-order optimization to achieve higher accuracy. We first investigate a Hessian-free optimization framework, and employ its principle to avoid direct usage of the Hessian matrix by computing its product with an arbitrary vector. We then propose the Hessian-free optimization-based LF model, which is able to extract latent factors from the given incomplete matrices via a second-order optimization process. Compared with LF models based on first-order optimization algorithms, experimental results on two industrial datasets show that the proposed one can offer higher prediction accuracy with reasonable computational efficiency. Hence, it is a promising model for implementing high-performance recommenders.

Original languageEnglish (US)
Article number7120953
Pages (from-to)946-956
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Issue number4
StatePublished - Aug 1 2015

All Science Journal Classification (ASJC) codes

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


  • Collaborative-filtering
  • Hessian-free Optimization
  • Incomplete Matrices
  • Latent Factor Model
  • Recommender Systems
  • Second-order Optimization


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