Phoenix: An Article Recommendation Engine

March 20, 2014

In today’s information age, we are all constantly barraged with content, from short tweets, to Buzzfeed-style listicles, longform journalism and other time consuming material. The rapid increase of the rate at which this content is generated makes it impossible for anyone to actually consume all the content published, and we as users must rely on recommendations to select how to spend our time when reading online. Through our work, we tried to predict whether a user would like an online article based on previous reading behavior. Specifically, we wanted to predict whether a user would mark an article as a favorite or not.

You can check out our project here.

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