This repository attempts to implement models for recommendation engines in TensorFlow using the Estimator API with feature columns.
The trainer module in this repository also allows for distributed model training and evaluation on Google Cloud Platform. Please refer to distributed.
- Linear classifer:
linear.py - DNN classifier:
deep.py - Linear & DNN classifier:
linear_deep.py - DeepFM:
deep_fm.py
categorical_columns: categorical feature columns inputnumeric_columns: numeric feature columns inputuse_linear: flag to include linear structure of model (default:True)use_mf: flag to include factorisation machine structure of model (default:True)use_dnn: flag to include deep structure of model (default:True)embedding_size: embedding size of latent factors (default:4)hidden_units: layer sizes of hidden units of deep structure (default:[16, 16])activation_fn: activation function of deep structure (default:tf.nn.relu)dropout: dropout rate of deep structure (default:0)optimizer: learning optimiser (default:"Adam")learning_rate: learning rate (default:0.001)
# clone repo
git clone git@github.com:yxtay/recommender-tensorflow.git && cd recommender-tensorflow
# create conda environment
conda env create -f=environment.yml
# activate environment
source activate dlThe MovieLens 100K Dataset is used for demonstration purpose. The following script downloads the data, processes and enriches it with a few basic features and serialises it to csv.
# downloads and processes movielens 100k dataset
python -m src.data.ml_100k localUsage
python -m src.data.ml_100k local -h
usage: ml_100k.py local [-h] [--url URL] [--dest DEST] [--log-path LOG_PATH]
optional arguments:
-h, --help show this help message and exit
--url URL url of MovieLens 100k data (default:
http://files.grouplens.org/datasets/movielens/ml-
100k.zip)
--dest DEST destination directory for downloaded and extracted
files (default: data)
--log-path LOG_PATH path of log file (default: main.log)
Usage
python -m trainers.deep_fm -h
usage: deep_fm.py [-h] [--train-csv TRAIN_CSV] [--test-csv TEST_CSV]
[--job-dir JOB_DIR] [--restore] [--exclude-linear]
[--exclude-mf] [--exclude-dnn]
[--embedding-size EMBEDDING_SIZE]
[--hidden-units HIDDEN_UNITS [HIDDEN_UNITS ...]]
[--dropout DROPOUT] [--batch-size BATCH_SIZE]
[--train-steps TRAIN_STEPS]
optional arguments:
-h, --help show this help message and exit
--train-csv TRAIN_CSV
path to the training csv data (default: data/ml-
100k/train.csv)
--test-csv TEST_CSV path to the test csv data (default: data/ml-
100k/test.csv)
--job-dir JOB_DIR job directory (default: checkpoints/deep_fm)
--restore whether to restore from job_dir
--exclude-linear flag to exclude linear component (default: False)
--exclude-mf flag to exclude mf component (default: False)
--exclude-dnn flag to exclude dnn component (default: False)
--embedding-size EMBEDDING_SIZE
embedding size (default: 4)
--hidden-units HIDDEN_UNITS [HIDDEN_UNITS ...]
hidden layer specification (default: [16, 16])
--dropout DROPOUT dropout rate (default: 0.1)
--batch-size BATCH_SIZE
batch size (default: 32)
--train-steps TRAIN_STEPS
number of training steps (default: 20000)
You may inspect model training metrics with Tensorboard.
tensorboard --logdir checkpoints/# linear model
python -m trainers.linear
# deep model
python -m trainers.deep
# wide & deep model
python -m trainers.linear_deepFor distributed model training and evaluation, please refer to distributed.
- Harper F. M., & Konstan, J. A. (2015). The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems, 5(4), Article 19, 19 pages. DOI=http://dx.doi.org/10.1145/2827872.
- Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., ... Shah, H. (2016). Wide & Deep Learning for Recommender Systems. arXiv:1606.07792 [cs.LG].
- Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. arXiv:1703.04247 [cs.IR].