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Collaborative Filtering: Item-Item collaborative filtering and User-User collaborative filtering
Recommendation Systems
analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations on new articles they will like.
We explore 2 methodologies of designing a recommendation system- Content based and using Collaborative Filtering
Performed EDA, created user-article matrix, calculated similarity using dot product, implemented Rank-Based, User-User CF, Content-Based, and Matrix Factorization, evaluated model with precision, recall, and F1-score.
Built recommender system for IBM. Rank-based recommendation, user-user based collaborative filtering, and matrix factorization are used.