Scikit-Learn: While primarily known for general machine learning, Scikit-Learn can be used to build recommendation systems using algorithms like matrix factorization. It’s well-suited for smaller, less complex datasets.
Surprise: A Python scikit for building and analyzing recommender systems that deal with explicit rating data. It is very user-friendly and provides tools to evaluate, analyze, and compare the performance of different algorithms.
TensorFlow Recommenders (TFRS): An extension of TensorFlow that simplifies the process of building, evaluating, and serving sophisticated recommendation models. It supports both retrieval (finding a set of candidates out of all possible candidates) and ranking.
LightFM: A Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback. It also integrates both item and user metadata into the traditional matrix factorization algorithms.
Spotlight: Uses PyTorch to build deep learning models for recommendations. It handles both implicit and explicit data and can also incorporate item and user metadata.
RecommenderLab: Although less popular than the others, this library is useful for developing and testing recommendation algorithms using techniques like collaborative filtering and matrix factorization.
PyTorch Geometric Temporal: Especially good for dynamic and temporal graphs, which can be very useful in certain types of recommendation systems, such as those that need to model user interests that change over time.