Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors

Xin Luo, Hao Wu, Huaqiang Yuan, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

206 Scopus citations


Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns hidden in such dynamic data for predicting missing ones with high accuracy. However, currently latent factor (LF) analysis-based QoS-predictors are mostly defined on static QoS data without the consideration of such temporal dynamics. To address this issue, this paper presents a biased non-negative latent factorization of tensors (BNLFTs) model for temporal pattern-aware QoS prediction. Its main idea is fourfold: 1) incorporating linear biases into the model for describing QoS fluctuations; 2) constraining the model to be non-negative for describing QoS non-negativity; 3) deducing a single LF-dependent, non-negative, and multiplicative update scheme for training the model; and 4) incorporating an alternating direction method into the model for faster convergence. The empirical studies on two dynamic QoS datasets from real applications show that compared with the state-of-the-art QoS-predictors, BNLFT represents temporal patterns more precisely with high computational efficiency, thereby achieving the most accurate predictions for missing QoS data.

Original languageEnglish (US)
Article number8681714
Pages (from-to)1798-1809
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number5
StatePublished - May 1 2020

All Science Journal Classification (ASJC) codes

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


  • Latent factor analysis (LFA)
  • latent factorization of tensor
  • learning temporal pattern
  • linear bias (LB)
  • non-negative latent factorization of tensor
  • non-negativity constraint
  • quality-of-service (QoS) prediction


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