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.
Recommendation Systems
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.
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
Built recommender system for IBM. Rank-based recommendation, user-user based collaborative filtering, and matrix factorization are used.
Add a description, image, and links to the user-user-filtering topic page so that developers can more easily learn about it.
To associate your repository with the user-user-filtering topic, visit your repo's landing page and select "manage topics."