An implement of Factorization Machines based on Scala and Spark MLlib.
Factorization Machine is a kind of state-of-the-art machine learning algorithm for multi-linear regression, and is widely used in recommendation systems.
Factorization Machines algorithm and its C++ implement LibFM (http://libfm.org/) works well in recent years' recommendation competitions.

A FMModel consist of three parts:
an Intercept (optional),
an one-way interactions weights like other linear models (optional),
a numFactors * numFeatures matrix representing the factors of each feature (mandatory).

I implement the training algorithm with SGD provided by MLlib's GradientDescent (to use MLlib's SGD, the model is encoded to a dense vector during training).

Ref:
Steffen Rendle (2012): Factorization Machines with libFM, in ACM Trans. Intell. Syst. Technol., 3(3), May. [PDF]