A recommender system is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.

• Collaborative filtering
• Content-based filtering
• Hybrid

Here i will use some interesting technics with explanation in notebook.

• Data needed to build a recommender
• Libraries available in Python to build recommenders
• Use cases

Used movies 100k dataset is a stable benchmark dataset with 100,000 ratings given by 944 users for 1664 movies, with each user having rated at least 20 movies.

Setting up the environment: python==3+

Libraries used:

• scikit-surprise
• pandas
• numpy
• scipy
• matplotlib

surprise library overview: https://www.youtube.com/watch?v=z0dx-YckFko&t=297s