Tracking and summarizing news on a daily basis with Columbia's Newsblaster
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Fair news reader: recommending news articles with different sentiments based on user preference
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
User preference modeling based on interest and impressions for news portal site systems
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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We developed a novel web application called “My Portal Viewer (MPV)”, which automatically categorizes and integrates meta-data from many news pages based on the user's preferences after gathering these news pages from various news sites. Our unique approach is based on two points: one is an automatic categorization of collected information based on user's interests and knowledge, and the other is the look and feel of the MPV page, which is applied to the user's favorite news portal page, and part of the original content is replaced by the integrated content. Whenever a user accesses the MPV page after browsing news pages, he/she can obtain the desired content efficiently because the MPV presents pages refreshed based on the user's behavior through his/her favorite page layout, which reflects his/her interests and knowledge. In this paper, we describe the MPV framework, and methods that are based on the user's preferences for replacing and categorizing content have been developed using an HTML table model and a vector matching model.