RecStudio is a unified, highly-modularized and recommendation-efficient recommendation library based on PyTorch.
RecStudio is also equipped with a web service, where the recommendation pipeline can be quickly established and visually evaluated on selected datasets, and the evaluation results are automatically archived and visualized in a leaderboard.
Customize your model like building blocks using the modules in RecStudio, such as query/item encoders, loss functions, scorers, samplers and so on.
Get different types of datasets for different types of models through unified dataset config.
RecStudio is also equipped with a web service.
Select one dataset and drag models to form your own recommendation pipline quickly, and then you can run it visually.
We save some results of models on different datasets into database.
Select a dataset and a metric, you can see the leaderboard of models on this dataset.
Go to Leaderboard