recbox 0.0.2

A configurable, tunable, and reproducible library for candidate item matching

MatchBox

Industrial recommender systems typically have two main phases: matching and ranking. In the first phase, candidate item matching (also known as candidate retrieval) aims for efficient and high-recall retrieval from a large item corpus. MatchBox provides an open source library for candidate item matching, with stunning features in configurability, tunability, and reproducibility.

Model Zoo

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

Dependency

We suggest to use the following environment where we test MatchBox only.

• python 3.6.x
• torch 1.0.x
• PyYAML<5.0
• pandas
• scikit-learn
• numpy
• h5py
• tqdm

Get Started

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 results, you can run the benchmarking script with the corresponding configs as follows.

• --config: The config directory where dataset config and model config are located.
• --expid: The experiment id defined in a model config file to denote a specific setting of hyper-parameters.
• --gpu: The gpu index used for experiment, and -1 for CPU.

cd model_zoo/SimpleX
python run_expid.py --config ./config/SimpleX_yelp18_m1 --expid SimpleX_yelp18_m1 --gpu 0
python run_expid.py --config ./config/SimpleX_amazonbooks_m1 --expid SimpleX_amazonbooks_m1 --gpu 0
python run_expid.py --config ./config/SimpleX_gowalla_m1 --expid SimpleX_gowalla_m1 --gpu 0

The running logs are also available in each config directory.