FRINK Linked Data Fragments server

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• W2544897543 abstract "With regard to rapid development of Internet technology and the increasing volume of data and information, the need for systems that can guide users toward their desired items and services may be felt more than ever. As a result, service-relevant data become too big to be effectively processed by traditional approaches. A naive solution is to decrease the number of services that need to be processed in real time. Clustering are such techniques that can reduce the data size by a large factor by grouping similar services together. In this research work, we propose a novel approach called Fuzzy C-Means Clustering-based Collaborative Filtering approach (FCM based CF) to overcome the above mentioned issues. Our proposed mechanism consists of two stages: FCM clustering and collaborative filtering. Clustering is a preprocessing step to separate the big data into manageable parts. Collaborative Filtering is imposed on one of the clusters. The main goal of this research is with respect to users, consolidating and mining their implicit interests from usage reviews or records may be a complement to the explicit interests (ratings). By this means, recommendations can be generated even if there are only few ratings. Finally, our proposed algorithm consolidates the web services and suggests the better web services based on the user navigation." @default.
• W2544897543 created "2016-11-04" @default.
• W2544897543 creator A5027499930 @default.
• W2544897543 creator A5077693683 @default.
• W2544897543 date "2015-02-01" @default.
• W2544897543 modified "2023-09-23" @default.
• W2544897543 title "FCM based CF: An efficient approach for consolidating big data applications" @default.
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• W2544897543 doi "https://doi.org/10.1109/iciict.2015.7396090" @default.
• W2544897543 hasPublicationYear "2015" @default.
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