Incorporating popularity in a personalized news recommender system

被引：33

作者：

Jonnalagedda, Nirmal ^{[1
]}

Gauch, Susan ^{[1
]}

Labille, Kevin ^{[1
]}

Alfarhood, Sultan ^{[1
]}

机构：

[1] Univ Arkansas, Comp Sci & Comp Engn, Fayetteville, AR 72701 USA

来源：

PEERJ COMPUTER SCIENCE | 2016年 / 2016卷 / 06期

关键词：

Twitter; Personalized news recommendation; News recommender systems; User profile;

D O I：

10.7717/peerj-cs.63

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

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