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Incorporating popularity in a personalized news recommender system
PeerJ Computer Science
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 adoi:10.7717/peerj-cs.63 fatcat:kjxwfwbmozgb3muooif6l6a4pe