Collaborative Filtering: Item-Item collaborative filtering and User-User collaborative filtering
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Updated
May 16, 2024 - Jupyter Notebook
Collaborative Filtering: Item-Item collaborative filtering and User-User 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.
Recommendation Systems
We explore 2 methodologies of designing a recommendation system- Content based and using Collaborative Filtering
Built recommender system for IBM. Rank-based recommendation, user-user based collaborative filtering, and matrix factorization are used.
analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations on new articles they will like.
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