Analyzed the interactions that users have with articles on IBM Watson Studio platform and compared various methods for a new article recommendation engine.
Simply recommends the most popular articles based on the most interactions since there are no ratings for the articles on this platform.
Determines similarity between users and then recommends new articles from similar users.
Builds out a matrix decomposition based on user-article interactions for recommending articles.
This project is part of Udacity's Data Scientist Nanodegree and as such uses some starter code and verbiage provided by Udacity. Additionally, the datasets are provided by IBM.