REPO DESCRIPTION
Our FM implementation is based on tensorflow 1.12.0.
You can run our reference training code on-the-fly using the following command:
#--------------------------how to train----------------------------#
./train.sh <training_path> <validation_path> <save_model_dir> <batch_size> <embedding_size> \
<optimizer [adagrad, adam]> <lr> <task [finish, like]> <track [1, 2]>
CODE STRUCTURE
#--------------------------run script------------------------------#
train.sh
#----------------------------train---------------------------------#
train.py
#------------------------common operation--------------------------#
common/
model_args.py
#--------convert input text data into tensorflow batch need--------#
data_io/
data_parser.py
#-------------prepare model and build up main framework------------#
models/
model.py
#---------------common algorithm and models for recom--------------#
model_zoo/
fm.py
#-----------------utils for str or data processing-----------------#
utils/
utils.py
ALGORITHM: FACTORIZATION MACHINE
BASELINE
Our baseline results with 5 features (user_id, user_city, item_id,author_id,item_city):
- TRACK2 LIKE TASK:
auc: 86.5%
#------------------------params-------------------------#
embedding_size = 40
optimizer = adam
lr = 0.0005
- TRACK FINISH TASK:
auc: 69.8%
#------------------------params-------------------------#
embedding_size = 40
optimizer = adam
lr = 0.0001