Collaborative Filtering: Item-Item collaborative filtering and User-User collaborative filtering
This project explores diverse "Recommendation Techniques", each offering a distinct approach to predicting user preferences.
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
A comparative analysis with RMSE on four foundational recommender system algorithms (user-user CF, item-item CF, content-based, SVD latent factor)
analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations on new articles they will like.
Built recommender system for IBM. Rank-based recommendation, user-user based collaborative filtering, and matrix factorization are used.
We explore 2 methodologies of designing a recommendation system- Content based and using Collaborative Filtering
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