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Exploring the effectiveness of true abnormal data elimination in context-aware web services recommendation

Fan, Xiaoliang, Wang, Yujie, Hu, Yakun, Ma, You and Liu, Xiao 2016, Exploring the effectiveness of true abnormal data elimination in context-aware web services recommendation, in ICWS 2016 : Proceedings of the IEEE International Conference on Web Services, IEEE, Piscataway, N.J., pp. 300-307, doi: 10.1109/ICWS.2016.46.

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Title Exploring the effectiveness of true abnormal data elimination in context-aware web services recommendation
Author(s) Fan, Xiaoliang
Wang, Yujie
Hu, Yakun
Ma, You
Liu, XiaoORCID iD for Liu, Xiao orcid.org/0000-0002-4151-8522
Conference name IEEE Web Services. International Conference (23rd : 2016 : San Francisco, California)
Conference location San Francisco, California
Conference dates 27 Jun.- 2 Jul. 2016
Title of proceedings ICWS 2016 : Proceedings of the IEEE International Conference on Web Services
Editor(s) Reiff-Marganiec, Stephan
Publication date 2016
Conference series IEEE Web Services
Start page 300
End page 307
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) context awareness
web services recommendation
QoS
true abnormal dta elimination
QoS prediction
true abnormal data elimination
Summary Recent years have witnessed a growing interest in context-aware recommender system (CARS), which explores the impact of context factors on personalized Web services recommendation. Basically, the general idea of CARS methods is to mine historical service invocation records through the process of context-aware similarity computation. It is observed that traditional similarity mining process would very likely generate relatively big deviations of QoS values, due to the dynamic change of contexts. As a consequence, including a considerable amount of deviated QoS values in the similarity calculation would probably result in a poor accuracy for predicting unknown QoS values. In allusion to this problem, this paper first distinguishes two definitions of Abnormal Data and True Abnormal Data, the latter of which should be eliminated. Second, we propose a novel CASR-TADE method by incorporating the effectiveness of True Abnormal Data Elimination into context-aware Web services recommendation. Finally, the experimental evaluations on a real-world Web services dataset show that the proposed CASR-TADE method significantly outperforms other existing approaches.
ISBN 9781509026753
Language eng
DOI 10.1109/ICWS.2016.46
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089699

Document type: Conference Paper
Collection: School of Information Technology
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