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

TL;DR

RecBole is a unified, efficient, and comprehensive open-source framework for developing and benchmarking a wide range of recommendation algorithms using PyTorch, facilitating standardization and reproducibility in research.

Contribution

It introduces RecBole, a library that standardizes implementation, evaluation, and benchmarking of 73 recommendation models across diverse datasets.

Findings

Implemented 73 models on 28 datasets

Supports GPU acceleration and automated tuning

Provides extensive evaluation protocols

Abstract

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/.