Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 27, Issue 3, Pages 524-537Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2412037
Keywords
Collaborative filtering; ensemble; latent factor; QoS prediction; Web-service selection
Categories
Funding
- Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah [23-135-35-HiCi]
- NSFC [61202347, 61272194, 61472051]
- Hong Kong, Macao and Taiwan Science and Technology Cooperation Program of China [2013DFM10100]
- U.S. NSF [CMMI-1162482]
- Young Scientist Foundation of Chongqing [cstc2014kjrc-qnrc40005, cstc2013kjrc-qnrc0079]
- Post-Doctoral Science Funded Project of Chongqing [Xm2014043]
- China Post-Doctoral Science Foundation [2014M562284]
- Fundamental Research Funds for the Central Universities [CDJZR12180012, 106112014CDJZR185503]
- Specialized Research Fund for the Doctoral Program of Higher Education [20120191120030]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1162482] Funding Source: National Science Foundation
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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.
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