We deign general and extensible data structures to unify the formatting and usage of various recommendation datasets.
We implement 72 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 ].
Our library includes 65 recommendation algorithms, covering four major categories: General Recommendation, Sequential Recommendation, Context-aware Recommendation, and Knowledge-based Recommendation, 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 ].
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 ].
Bole is a famous Chinese judge of qianlima (swift horse) in Spring and Autumn period.