Faculty Advisor

Claypool, Mark L.

Faculty Advisor

Rundensteiner, Elke

Abstract

Many online newspapers provide news without consideration for reader's tastes. Collaborative filtering solves this problem by recommending articles based upon ratings by others. Collaborative filtering can be enhanced by content-based filtering when ratings are sparse. We applied a blend of collaborative and content-based filtering to an online newspaper, the Worcester Telegram and Gazette. Our system consists of a web-based interface, a back-end recommendation engine, and a database. We analyze results of the project and propose direction for future work.

Publisher

Worcester Polytechnic Institute

Date Accepted

January 1999

Major

Computer Science

Project Type

Major Qualifying Project

Accessibility

Restricted-WPI community only

Advisor Department

Computer Science

Advisor Program

Computer Science

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