A New QoS-Aware Web Service Recommendation System Based on Contextual Feature Recognition at Server-Side

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A New QoS-Aware Web Service Recommendation System Based on Contextual Feature Recognition at Server-Side

A New QoS-Aware Web Service Recommendation System Based on Contextual Feature Recognition at Server-Side

Abstract

In This Project A New QoS-Aware Web Service Recommendation System Based on Server-Side Contextual Feature Recognition, Quality of Service (QoS) has been playing an increasingly important role in today’s Web Service environment.

Introduction

Many developers are currently searching services through public sites such as Google Developers (developers.google.com), Yahoo! Pipes (pipes.yahoo.com), ProgrammableWeb (programmableweb.com).projects on A New QoS-Aware Web System Some web services are available only in the EU, so it is not possible to ship software using these services to other countries.

Deployment of service-oriented software can be at great risk without knowledge of these things.Since selecting a high-quality Web service among a large number of candidates is a non-trivial task, some developers choose to implement their own services rather than using those available to the public, which entails additional time and resource overhead.On the other hand, using an inappropriate service can add potential risks to the business process.

                                          Conclusion

This A New QoS-Aware Web Service Recommendation System based on Server-Side’s Contextual Feature Recognition paper proposes a new Web Service Recommendation System to make personalized QoS value predictions.

Projects on A New QoS-Aware Web System The proposed approach takes full advantage of user preferential information and web service features simultaneously to achieve higher prediction accuracy than other approaches.

The results of this project suggest some promising avenues for future research.For example, designing and creating recommendation systems that consider the impacts of Web service locations and infer personal preferences from social tags of a customer.

Future investigation also deserves the applications of the proposed framework within the mobile environment. We use WSDL files in this paper to extract service features.This is due to the lack of contextual information about the real-world datasets.We are currently exploring the development of online applications to collect more contextual information from Web services such as tags,time, and so on.