Personalized News Recommendation Using Twitter
By Nirmal Jonnalagedda, Susan Gauch. 2013
58 citations recorded by the source corpus.
Online news reading has become a popular way to read news articles from a huge collection of news sources around the globe. News recommender systems help users manage this flood by suggesting 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 with the help of the tweets 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 combining these two approaches, we present a hybrid news recommendation model that recommends interesting news stories to the user based on their popularity as well as their relevance to the user profile.
Subfields: Computer Vision and Pattern Recognition, Information Systems
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In Computer Vision and Pattern Recognition
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Citations in the corpus
- Representation learning for very short texts using weighted word embedding aggregation (2016, background): “Especially on social media, millions of such short texts are sent every day, and it quickly becomes a daunting task to find similar messages among them, which is at the core of applications such as event detection (De Boom et al. (2015b)), news recommendation (Jonnalagedda and Gauch (2013)), etc.”