User-based collaborative filtering¶

Python notebook: https://github.com/daviskregers/data-science-recap/blob/main/19-movie-similarity-recommender-systems.ipynb

• build a matrix of things each user bought/viewed/rated
• Compute a similarity scores between users
• Find users similar to you
• Recommend stuff they bought/viewed/rated that you haven't yet.

Problems with User-Based CF¶

• People are fickle, tastes change
• There are usually many more people than things - computationally more expensive than it should most of the time.
• People do bad things - easy to manipulate by creating fake personas on the platform.