Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models

Xin Luo, Meng Chu Zhou, Yunni Xia, Qingsheng Zhu, Ahmed Chiheb Ammari, Ahmed Alabdulwahab

Research output: Contribution to journalArticlepeer-review

227 Scopus citations


Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work aims at making highly accurate predictions for missing QoS data via building an ensemble of nonnegative latent factor (NLF) models. Its motivations are: 1) the fulfillment of nonnegativity constraints can better represent the positive value nature of QoS data, thereby boosting the prediction accuracy and 2) since QoS prediction is a learning task, it is promising to further improve the prediction accuracy with a carefully designed ensemble model. To achieve this, we first implement an NLF model for QoS prediction. This model is then diversified through feature sampling and randomness injection to form a diversified NLF model, based on which an ensemble is built. Comparison results between the proposed ensemble and several widely employed and state-of-the-art QoS predictors on two large, real data sets demonstrate that the former can outperform the latter well in terms of prediction accuracy.

Original languageEnglish (US)
Article number7091940
Pages (from-to)524-537
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number3
StatePublished - Mar 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


  • Collaborative filtering
  • QoS prediction
  • Web-service selection.
  • ensemble
  • latent factor


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