Date of Graduation

5-2013

Document Type

Thesis

Degree Name

Master of Science in Computer Science (MS)

Degree Level

Graduate

Department

Computer Science & Computer Engineering

Advisor

Susan Gauch

Committee Member

Craig Thompson

Second Committee Member

Bajendra Panda

Keywords

Applied sciences

Abstract

Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily swamped by information of little interest to them. News recommender systems are one approach to help users find interesting articles to read. News recommender systems present the articles to individual users based on their interests rather than presenting articles in order of their occurrence. In this thesis, we present our research on developing personalized news recommendation system with the help of a popular micro-blogging service "Twitter". The news articles are ranked based on the popularity of the article that is identified with the help of the tweets from the Twitter's public timeline. Also, user profiles are built based on the user's interests and the news articles are ranked by matching the characteristics of the user profile. With the help of these two approaches, we present a hybrid news recommendation model that recommends interesting news stories to the user based on their popularity and their relevance to the user profile.

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