Incorporating popularity in a personalized news recommender system | Zendy

Nirmal Jonnalagedda | Zendy; Susan Gauch | Zendy; Kevin Labille | Zendy; Sultan Alfarhood | Zendy

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Incorporating popularity in a personalized news recommender system

Author(s) -

Nirmal Jonnalagedda,

Susan Gauch,

Kevin Labille,

Sultan Alfarhood

Publication year - 2016

Publication title -

peerj computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.806

H-Index - 24

ISSN - 2376-5992

DOI - 10.7717/peerj-cs.63

Subject(s) - popularity , timeline , computer science , world wide web , recommender system , globe , construct (python library) , news aggregator , news media , reading (process) , relevance (law) , internet privacy , advertising , political science , business , geography , psychology , archaeology , neuroscience , law , programming language

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 overwhelmed by information of little interest to them. News recommender systems help users manage this flood by recommending articles based on user interests rather than presenting articles in order of their occurrence. We present our research on developing personalized news recommendation system with the help of a popular micro-blogging service, “Twitter.” News articles are ranked based on the popularity of the article identified from Twitter’s public timeline. In addition, users construct profiles based on their interests and news articles are also ranked based on their match to the user profile. By integrating these two approaches, we present a hybrid news recommendation model that recommends interesting news articles to the user based on their popularity as well as their relevance to the user profile

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