Advanced Recommender Systems with Python
I implemented Model-Based CF by using singular value decomposition (SVD) and Memory-Based CF by computing cosine similarity. I used famous MovieLens dataset, which is one of the most common datasets used when implementing and testing recommender engines. It contains 100k movie ratings from 943 users and a selection of 1682 movies.
All processes and their appropriate explanations are in jupyter notebook.