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RecBole is a unified, comprehensive and efficient framework developed based on PyTorch. It aims to help the researchers to reproduce and develop recommendation models.
In the lastest release, our library includes 77 recommendation algorithms [Model List], covering four major categories:
We design a unified and flexible data file format, and provide the support for 28 benchmark recommendation datasets [Collected Datasets]. A user can apply the provided script to process the original data copy, or simply download the processed datasets by our team.
- General and extensible data structure
We deign general and extensible data structures to unify the formatting and usage of various recommendation datasets.
- Comprehensive benchmark models and datasets
We implement 77 commonly used recommendation algorithms, and provide the formatted copies of 28 recommendation datasets.
- Efficient GPU-accelerated execution
We design many tailored strategies in the GPU environment to enhance the efficiency of our library.
- Extensive and standard evaluation protocols
We support a series of commonly used evaluation protocols or settings for testing and comparing recommendation algorithms.
RecBole is developed and maintained by RUC, BUPT, ECNU.
Here is the list of our lead developers in each development phase. They are the souls of RecBole and have made outstanding contributions.
June 2020 ~ Nov. 2020
Nov. 2020 ~ Now
v0.1.2 ~ v1.0.0
RecBole uses MIT License.