Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Srinivasa, KG and Sharath, S and Venugopal, KR and Patnaik, Lalit M (2007) Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines. In: Intelligent Data Analysis, 11 (5). 481 -496.

PDF se.pdf - Published Version Restricted to Registered users only Download (319Kb) | Request a copy

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.

Item Type:	Journal Article
Additional Information:	Copyright of this article belongs to IOS Press.
Keywords:	selective dissemination;xml;genetic algorithms;Support Vector Machines.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited:	27 Apr 2010 10:34
Last Modified:	19 Sep 2010 06:00
URI:	http://eprints.iisc.ernet.in/id/eprint/27293

Actions (login required)

View Item