Research Article | OPEN ACCESS
Collaborative Filtering Based Hybrid Approach for Web Service Recommendations
1G. Vadivelou and 2E. Ilavarasan
1Department of Computer Science and Engineering, Bharathiar University, Coimbatore, Tamilnadu, India
2Department of Computer Science and Engineering, Pondicherry Engineering College, Pondicherry, India
Research Journal of Applied Sciences, Engineering and Technology 2014 5:615-622
Received: April 22, 2014 | Accepted: June 02, 2014 | Published: August 05, 2014
Abstract
Now a days, Web services are becoming the primary source for constructing software system over Internet. The quality of whole system greatly dependents on the QoS of single web service, so QoS information is an important indicator for service selection. But in reality, QoSs of some Web Services may be unavailable for users. How to predicate the missing QoS value of Web service through fully using the existing information is a difficult problem. This study first proposes a novel method for clustering similar web services using semantic approach in order to overcome the limitations of pattern-matching approach and finally proposes a cluster based approach using Slope One Collaborative filtering method to predict the QoS values for similar web services for similar users. Proposed approaches are applied on a dataset consisting of WSDL files and QoS values for various QoS parameters which are collected from the Internet and the proposed approaches shows better qualities with respect to clusters formed and the QoS values predicted.
Keywords:
Clustering , collaborative filtering , slope one , web service , WSDL,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
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