Personal News RSS Feeds Generation Using Existing News Feeds

Authors:
Bin Liu;Hao Han;Tomoya Noro;Takehiro Tokuda
Affiliations:
Department of Computer Science, Tokyo Institute of Technology Meguro, Tokyo, Japan 152-8552;Department of Computer Science, Tokyo Institute of Technology Meguro, Tokyo, Japan 152-8552;Department of Computer Science, Tokyo Institute of Technology Meguro, Tokyo, Japan 152-8552;Department of Computer Science, Tokyo Institute of Technology Meguro, Tokyo, Japan 152-8552
Venue:
ICWE '9 Proceedings of the 9th International Conference on Web Engineering
Year:
2009

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Abstract

Nowadays more and more news sites publish news stories using news RSS feeds for easier access and subscription on the Web. Generally, news stories are grouped by several categories and each category corresponds to one news RSS feed. However there are no uniform standards for categorization. Each news site has its own way of categorization for grouping news stories. These dissimilar categorization can not always satisfy every individual user, and generally the provided categories are not detailed enough for personal using. In this paper, we proposed a method for users to create customizable personal news RSS feeds using existing ones. We implemented a news directory system(NDS) which can retrieve news stories by RSS feeds and classify them. Using this system, we can recategorize news stories from original RSS feeds, or subdivide one RSS feed to a more detailed level. With the classification information for each news article, we offer customizable personal news RSS feeds to subscribers.