Collaborative methodologies for pattern evaluation for web personalization using Semantic Web Mining

Abstract

The process of semantic web mining is very much applicable in social media and networking sites which will mostly result in overloading of the content. The personalized system is required basically to deal with large information system in order to perform information filteration. The user through internet and web media are considered as content therefore internet and social networking media are the optimal way to express bulk of contents. The collaborative filtering techniques compute the ratings and recommendations which is purely based on information about similar user items and their content. The proposed work is a combined technique which results into hybrid approach, where the feature of the content extracted from open linked dataset, and result in better accuracy in the prediction and analysis. A hybrid prototype is proposed and will be implemented in Weka as extension of the work. The work discusses the role and social media in web mining and advantages of content feature retrieval for semantic web mining methodology.