Abstract: Web services are integrated software components for the support of interoperable machine-to-machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems (e.g., ill-suited performance) to the resulting applications. In this paper, we propose a novel collaborative filtering-based Web service recommender system to help users select services with optimal Quality-of-Service (QoS) performance. This system provides a QoS-aware Web service recommendation approach. The basic idea is to predict Web service QoS values and recommend the best one for active users based on historical Web service QoS records. Thus we can improve the recommendation accuracy and time complexity compared with existing service recommendation algorithms. Proposed method uses enriched NLP protocols to get the recommendation from the user comments. System successfully merges web service ranking and user comments to provide best hybrid solution for proper recommendation.
Keyword: Web Service, Quality of Service (QoS), Recommendation, Collaborative Filtering, Pearson’s Correlation.
Title: Web Service Recommendation via Quality of Service Information
Author: Rajeshree Mohalkar, Kshiteeja Fadtare, Shweta Todkar
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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