Heterogeneous ensemble learning method for personalized semantic web service recommendation
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ORIGINAL RESEARCH
Heterogeneous ensemble learning method for personalized semantic web service recommendation S. Sagayaraj1
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M. Santhoshkumar1
Received: 9 August 2019 / Accepted: 19 May 2020 Ó Bharati Vidyapeeth’s Institute of Computer Applications and Management 2020
Abstract Semantic web service (SWS) discovery and recommendation (SWSR) has emerged as a potential technology which aims to fulfill the user requirements by providing an improved recommendation for Academic and business communities. In SWSR, the user search pattern is adopted to make service discovery as well as recommendation. In order to achieve the precise recommendation of SWS, the ensemble learning method is utilized. This method encompasses the elimination of in-appropriate features and selects the optimal features of the requirements for the Academic and business communities. Semantic analysis is the one of the dominant technologies for SWSR, but it has not yet been explored by applying the ensemble learning over the service features to make optimal selection of features and to provide personalized recommendation. With this motive, in this paper a Heterogeneous Ensemble Learning method for Semantic web service Personalization and recommendation (HELSWSR) framework has been proposed. It will revitalize the industries to select optimal services SWS discovery. HELSWSR assists the feature extractions, concatenation of features selection using user profiles and triples from the OWL-S files. This framework combines various methods that eventually ensure service selection through the Maximum Voting Ensemble (MVE) technique. The MVE helps to select the services and recommends the top-10 services. From that list, the Academic or Business communities can be able to predict the appropriate services. The proposed & S. Sagayaraj [email protected] M. Santhoshkumar [email protected] 1
Sacred Heart College (Autonomous), Tirupattur, India
framework performance is noticeably enhanced when compared with the traditional user search pattern technique. Keywords Semantic web service recommendation Userpersonalization Ensemble learning
1 Introduction The high pace rise in internet and software computing technologies have broadened the horizon of online human activities making it as an inevitable or irreplaceable need of modern time. In the era of World Wide Web, users explore or search online contents to make decision or to perform certain tasks. On the other hand, companies or business houses or academic use these technologies to propagate its product to the users in the form of goods or services. Functionally, up surging use of web-platforms or allied web search applications have been generating huge amount of web usage data encompassing web-search details, purchases, online views or related search history, services as platform etc.,. This huge data often called as web-data possesses significant potential for companies to exploit it for better business decision. On the other hand, web-search history or preferences by an user helps personalizing that e
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