This is a TensorFlow implementation of an arbitrary order (>=2) Factorization Machine based on paper Factorization Machines with libFM.

It supports:

• dense and sparse inputs
• different (gradient-based) optimization methods
• Classification/regression via different loss functions (logistic and mse implemented)
• logging via TensorBoard

The inference time is linear with respect to the number of features.

Tested on both Python2.7 and Python3.5.

Dependencies

• scikit-learn
• numpy
• tensorflow 0.8+
• tqdm

Usage

The interface is similar to scikit-learn models. To train a 6-order FM model with rank=10 for 100 iterations with learning_rate=0.01 use the following sample

from tffm import TFFMClassifier
model = TFFMClassifier(
    order=6,
    rank=10,
    optimizer=tf.train.AdamOptimizer(learning_rate=0.01),
    n_epochs=100,
    batch_size=-1,
    init_std=0.001,
    input_type='dense'
)
model.fit(X_tr, y_tr, show_progress=True)

See example.ipynb and gpu_benchmark.ipynb for more details.

Corresponding paper is coming soon.