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In recent years, there are a large number of recommendation algorithmsproposed in the literature, from traditional collaborative filtering to deeplearning algorithms. However, the concerns about how to standardize open sourceimplementation of recommendation algorithms continually increase in theresearch community. In the light of this challenge, we propose a unified,comprehensive and efficient recommender system library called RecBole, whichprovides a unified framework to develop and reproduce recommendation algorithmsfor research purpose. In this library, we implement 73 recommendation models on28 benchmark datasets, covering the categories of general recommendation,sequential recommendation, context-aware recommendation and knowledge-basedrecommendation. We implement the RecBole library based on PyTorch, which is oneof the most popular deep learning frameworks. Our library is featured in manyaspects, including general and extensible data structures, comprehensivebenchmark models and datasets, efficient GPU-accelerated execution, andextensive and standard evaluation protocols. We provide a series of auxiliaryfunctions, tools, and scripts to facilitate the use of this library, such asautomatic parameter tuning and break-point resume. Such a framework is usefulto standardize the implementation and evaluation of recommender systems. Theproject and documents are released at https://recbole.io/.

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms