OpenMatch

OpenMatch provides an open source library for candidate item matching with stunning features in configurability, tunability, and reproducibility.

Model List

Publication	Model	Paper
UAI'09	MF-BPR	BPR: Bayesian Personalized Ranking from Implicit Feedback
RecSys'16	YoutubeDNN	Deep Neural Networks for YouTube Recommendations
CIKM'21	MF-CCL / SimpleX	SimpleX: A Simple and Strong Baseline for Collaborative Filtering

Get Started

Run the demo

The code workflow is structured as follows:

# Set the data config and model config
feature_cols = [{...}] # define feature columns
label_col = {...} # define label column
params = {...} # set data params and model params

# Set the feature encoding specs
feature_encoder = FeatureEncoder(feature_cols, label_col, ...) # define the feature encoder
datasets.build_dataset(feature_encoder, ...) # fit feature_encoder and build dataset 

# Load data generators
train_gen, valid_gen, test_gen = h5_generator(feature_encoder, ...)

# Define a model
model = SimpleX(...)

# Train the model
model.fit(train_gen, valid_gen, ...)

# Evaluation
model.evaluate(test_gen)

Run the benchmark

For reproducing the experiment result, you can run the benchmarking script with the corresponding config file as follows.

• --config: The config file that defines the hyper-parameter space.
• --gpu: The gpu index used for experiment, and -1 for CPU.

cd benchmarks
python run_param_tuner.py --config Yelp18/SimpleX_yelp18_x0/SimpleX_yelp18_x0_tuner_config.yaml --gpu 0