Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data

Xin Luo, Meng Chu Zhou, Shuai Li, Yun Ni Xia, Zhu Hong You, Qing Sheng Zhu, Hareton Leung

Research output: Contribution to journalArticlepeer-review

192 Scopus citations


Generating highly accurate predictions for missing quality-of-service (QoS) data is an important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in dealing with it. However, they are based on first-order solvers that cannot well address their target problem that is inherently bilinear and nonconvex, thereby leaving a significant opportunity for accuracy improvement. This paper proposes to incorporate an efficient second-order solver into them to raise their accuracy. To do so, we adopt the principle of Hessian-free optimization and successfully avoid the direct manipulation of a Hessian matrix, by employing the efficiently obtainable product between its Gauss–Newton approximation and an arbitrary vector. Thus, the second-order information is innovatively integrated into them. Experimental results on two industrial QoS datasets indicate that compared with the state-of-the-art predictors, the newly proposed one achieves significantly higher prediction accuracy at the expense of affordable computational burden. Hence, it is especially suitable for industrial applications requiring high prediction accuracy of unknown QoS data.

Original languageEnglish (US)
Pages (from-to)1216-1228
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number4
StatePublished - Apr 1 2018

All Science Journal Classification (ASJC) codes

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


  • Big data
  • Web service
  • latent factor model
  • missing data prediction
  • quality-of-service (QoS)
  • second-order solver
  • service computing sparse matrices


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