fastFM

Uses the familiar fit/predict interface, making it easy to incorporate into existing machine learning pipelines, as demonstrated in the usage example with FMRegression.

Supports stochastic gradient descent, coordinate descent, and Markov Chain Monte Carlo, providing flexibility for different optimization needs and Bayesian inference, as outlined in the solver table.

Core algorithms are implemented in C and wrapped with Cython, ensuring fast execution on high-dimensional sparse data, which is critical for recommendation systems and CTR estimation.

The MCMC solver offers probabilistic predictions with uncertainty, a rare feature in FM implementations that is valuable for risk-aware applications like advertising.