C4.5: programs for machine learning
C4.5: programs for machine learning
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Collaborative Filtering for a Distributed Smart IC Card Community Support System
PRIMA 2001 Proceedings of the 4th Pacific Rim International Workshop on Multi-Agents, Intelligent Agents: Specification, Modeling, and Applications
Content-Based Filtering System for Music Data
SAINT-W '04 Proceedings of the 2004 Symposium on Applications and the Internet-Workshops (SAINT 2004 Workshops)
Service-Oriented Architecture: Concepts, Technology, and Design
Service-Oriented Architecture: Concepts, Technology, and Design
A QoS Based Web Service Selection Model
IFITA '09 Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 03
A qos-aware selection model for semantic web services
ICSOC'06 Proceedings of the 4th international conference on Service-Oriented Computing
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In recent years, as the Internet spreads, the use of the Web Service has increased, and it has diversified. The Web Service is registered with UDDI, and the user selects service there and can use it for the provider by making a demand. In future, if the Web Service comes to be used more widely, the number of Web Services will increase, and the number of registrations at the UDDI will also increase. The user examines the large number of available services, and needs to choose the service that best matches their purpose. Quality of Service (QoS) is used as an index when a user chooses a service. Many studies show that the scoring of QoS for service selection is important. Quality of Service is registered by the provider and is treated as an objective factor. However, subjective evaluation, the evaluation of the user after the service use, is also needed to choose the best service. In this study, we use a new element, evaluation, in addition to QoS for service selection. We have expanded the existing filtering technique to make a new way of recommending services. Our method incorporates subjective evaluation. With this model, we apply the technique of information filtering to the Web Service recommendation and make an agent. Also, we simulate it after having clarified the behavior and tested it. The results of testing show that the model provides high levels of precision.