Modeling user's non-functional preferences for personalized service ranking
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Personalised graph-based selection of web APIs
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Towards a folksonomy of web APIs
Proceedings of the 3rd International Workshop on Semantic Search Over the Web
A framework for guided search of mashup components
Proceedings of the 3rd International Workshop on Semantic Search Over the Web
Advanced Web API search patterns adding collective knowledge to public repository facets
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Ranking is an important step once automated discovery in Web Services is enabled, it allows for automated selection of the best matched service, out of the discovered ones. However, automated selection of the best matched service is not as simple as it may look like. Different service consumers may have different preferences to select the service providers, which may even depend upon their past interactions. Various approaches have been proposed that allow ranking of services based on different functional and non-functional aspects. However, we believe that the selection of services based on the analysis of the past interactions of service consumers or their social-network could be another effective way to rank the services for the benefit of service consumers. In this paper, we present a community-aware personalized approach for recommending and ranking Web Services for a service consumer. It is based on analysis of historical interactions among service consumers and service providers. We perform analysis and mining on the log information of service consumers and service providers, model their past interactions as social network, apply standard social-network analysis techniques, and use this information in ranking Web Services.