Predicting web service QoS via matrix-factorization-based collaborative filtering under non-negativity constraint

Xin Luo, Mengchu Zhou, Yunni Xia, Qingsheng Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Matrix-factorization based collaborative filtering is an efficient approach to the problem of user-side quality-of-service (QoS) prediction. In this work, we focus on building a matrix-factorization-based collaborative filtering model for QoS prediction under a non-negativity constraint. The motivation is that since QoS data such as response time, cost and throughput, are all positive, a non-negative model can better demonstrate their characteristics. By investigating a non-negative training process relying on each involved feature, we invent a non-negative latent factor model to deal with the sparse QoS matrix subject to the non-negativity constraint. We subsequently introduce Tikhonov regularization into it to obtain the regularized non-negative latent factor model. Their efficiency is proven by the experimental results on a large industrial dataset.

Original languageEnglish (US)
Title of host publication2014 23rd Wireless and Optical Communication Conference, WOCC 2014
PublisherIEEE Computer Society
ISBN (Print)9781479952496
DOIs
StatePublished - 2014
Event2014 23rd Wireless and Optical Communication Conference, WOCC 2014 - Newark, NJ, United States
Duration: May 9 2014May 10 2014

Publication series

Name2014 23rd Wireless and Optical Communication Conference, WOCC 2014

Other

Other2014 23rd Wireless and Optical Communication Conference, WOCC 2014
Country/TerritoryUnited States
CityNewark, NJ
Period5/9/145/10/14

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Keywords

  • Big Data
  • Collaborative Filtering
  • Matrix Factorization
  • Non-negativity
  • QoS-prediction

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