  In recent years, there are a large number of recommendation algorithms
proposed in the literature, from traditional collaborative filtering to deep
learning algorithms. However, the concerns about how to standardize open source
implementation of recommendation algorithms continually increase in the
research community. In the light of this challenge, we propose a unified,
comprehensive and efficient recommender system library called RecBole, which
provides a unified framework to develop and reproduce recommendation algorithms
for research purpose. In this library, we implement 73 recommendation models on
28 benchmark datasets, covering the categories of general recommendation,
sequential recommendation, context-aware recommendation and knowledge-based
recommendation. We implement the RecBole library based on PyTorch, which is one
of the most popular deep learning frameworks. Our library is featured in many
aspects, including general and extensible data structures, comprehensive
benchmark models and datasets, efficient GPU-accelerated execution, and
extensive and standard evaluation protocols. We provide a series of auxiliary
functions, tools, and scripts to facilitate the use of this library, such as
automatic parameter tuning and break-point resume. Such a framework is useful
to standardize the implementation and evaluation of recommender systems. The
project and documents are released at https://recbole.io/.
