In the previous section we saw implementations of user-based recommender systems and item-based recommender systems using the R package,
recommenderlab. In this section, we see UBCF and IBCF implementation using the Python programming language.
For this section, we use the MovieLens 100k dataset, which contains 943 user ratings on 1682 movies. Unlike in R, in Python we do not have a proper Python package dedicated to building recommender engines, at least the neighborhood-based recommenders such as user-based/item-based recommenders.
We have the Crab Python package available but it is not actively supported. So I thought of building a recommender engine using scientific packages in Python such as NumPy, sklearn, ...