Affiliations: [a] Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, 560001, India. E-mail: kgsrinivas@msrit.edu, venugopalkr@gmail.com | [b] Microprocessor Applications Laboratory, Indian Institute of Science, Bangalore, 560012, India. E-mail: lalit@micro.iisc.ernet.in
Abstract: Extensible Markup Language (XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine (SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.
Keywords: Selective dissemination, XML, Genetic Algorithms, Support Vector Machines
