RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
By Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Kaiyuan Li, Yushuo Chen, Yujie Lu, Yujie Lu, Haibo Wang, Changxin Tian and 10 more. 2020
601 citations recorded by the source corpus.
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 (pronounced as [rEk'boUl@r]), 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/.
Subfields: Artificial Intelligence, Information Systems, Evaluation & Benchmarks, Recommender Systems
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Citations in the corpus
- MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers (2021, background): “, 2021), and RecBole (Zhao et al., 2020) for such elegant and easy-to-use libraries.”
- Self-Supervised Learning for Recommender Systems: A Survey (2022, background): “While some open-source repositories such as RecBole [136] and QRec [64] have provided standard evaluation protocols, they are designed for universal purposes and their architectures are not ideal for implementing SSR models.”
- Knowledge Graph Contrastive Learning for Recommendation (2022, background): “Most of compared baselines are evaluated based on the unified recommendation library RecBole [54].”
- Large Language Models are Zero-Shot Rankers for Recommender Systems (2023, unknown): “Multiplicity’, examples in prompts [38, 22].”
- Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2022, methodology): “We implement the proposed model and all the baselines with RecBole2 [43], which is a unified opensource framework to develop and reproduce recommendation algorithms.”
- Towards Universal Sequence Representation Learning for Recommender Systems (2022, methodology): “We implement UniSRec using a popular open-source recommendation library RecBole2 [35].”
- Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, unknown): “In order to complete this evaluation, we adopt a straightforward approach for prospective testing purposes.”
- Filter-enhanced MLP is All You Need for Sequential Recommendation (2022, methodology): “Recommender systems [32, 67, 69, 71] have been widely deployed in online platforms (e.”