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