We deign general and extensible data structures to unify the formatting and usage of various recommendation datasets.
We implement more than 100 commonly used recommendation algorithms, and provide the formatted copies of 28 recommendation datasets.
We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms.
RecBole is developed based on
Python and PyTorch for
reproducing and developing recommendation algorithms
in a unified, comprehensive and efficient framework for
research purpose. It can be installed from pip, conda and source, and easy to
Detailed in [ Install RecBole ].
We have implemented more than 100 recommender system models, covering four common recommender system categories in RecBole and eight toolkits of RecBole2.0, including: General Recommendation, Sequential Recommendation, Context-aware Recommendation, Knowledge-based Recommendation and subpackages, which can support the basic research in recommender systems. Detailed in [ Model List ].
We design a unified and flexible data file format, and provide the support for 28 benchmark recommendation datasets. A user can apply the provided script to process the original data copy, or simply download the processed datasets by our team. Detailed in [ Dataset List ].
On the basis of RecBole, our team has continuously expanded and updated it from the perspective of data and model for different research directions, and launched eight extension toolkits in version 2.0. Until now, RecBole has a total of 11 related GitHub projects. Detailed in 【 RecBole2.0 】。
RecBole is freely open to universities, teachers, students and enthusiasts in recommender system. We will provide regular maintenance and update for RecBole. Anyone who interested in our project is welcome to join us. Let us build a much more wonderful open source community of recommender system! Detailed in [ About ].
RecBole is a famous Chinese judge of qianlima (swift horse) in Spring and Autumn period.