Introduction

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 first release, our library includes 65 recommendation algorithms [Model List], covering four major categories:

• General Recommendation

• Sequential Recommendation

• Context-aware Recommendation

• Knowledge-based Recommendation

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.

Features:

• 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 65 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.