Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/603708
Title: Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
Authors: Xin Luo;MengChu Zhou;Yunni Xia;Qingsheng Zhu;Ahmed Chiheb Ammari;Ahmed Alabdulwahab
subject: latent factor;ensemble;Web-service selection.;QoS prediction;Collaborative filtering
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/136039
http://localhost/handle/Hannan/603708
ISSN: 2162-237X
2162-2388
volume: 27
issue: 3
Appears in Collections:2016

Files in This Item:
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Title: Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
Authors: Xin Luo;MengChu Zhou;Yunni Xia;Qingsheng Zhu;Ahmed Chiheb Ammari;Ahmed Alabdulwahab
subject: latent factor;ensemble;Web-service selection.;QoS prediction;Collaborative filtering
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/136039
http://localhost/handle/Hannan/603708
ISSN: 2162-237X
2162-2388
volume: 27
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7091940.pdf2.89 MBAdobe PDFThumbnail
Preview File
Title: Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
Authors: Xin Luo;MengChu Zhou;Yunni Xia;Qingsheng Zhu;Ahmed Chiheb Ammari;Ahmed Alabdulwahab
subject: latent factor;ensemble;Web-service selection.;QoS prediction;Collaborative filtering
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/136039
http://localhost/handle/Hannan/603708
ISSN: 2162-237X
2162-2388
volume: 27
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7091940.pdf2.89 MBAdobe PDFThumbnail
Preview File