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

arXiv: https://arxiv.org/abs/2011.01731 · HTML: https://mlsanity.com/paper/2011.01731/

## Key facts

- Cited by 138 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 30 of 138)

- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach](https://mlsanity.com/paper/2305.07001/) (2023)
- [Towards Universal Sequence Representation Learning for Recommender Systems](https://mlsanity.com/paper/2206.05941/) (2022)
- [AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems](https://mlsanity.com/paper/2310.09233/) (2023)
- [Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://mlsanity.com/paper/2210.12316/) (2022)
- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning](https://mlsanity.com/paper/2202.06200/) (2022)
- [Knowledge Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2205.00976/) (2022)
- [Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation](https://mlsanity.com/paper/2307.09688/) (2023)
- [MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers](https://mlsanity.com/paper/2109.00799/) (2021)
- [CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://mlsanity.com/paper/2204.11067/) (2022)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Decoupled Side Information Fusion for Sequential Recommendation](https://mlsanity.com/paper/2204.11046/) (2022)
- [Can Small Language Models be Good Reasoners for Sequential Recommendation?](https://mlsanity.com/paper/2403.04260/) (2024)
- [AutoMLP: Automated MLP for Sequential Recommendations](https://mlsanity.com/paper/2303.06337/) (2023)
- [CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation](https://mlsanity.com/paper/2405.02429/) (2024)
- [A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation](https://mlsanity.com/paper/2207.07483/) (2022)
- [Top-N Recommendation with Counterfactual User Preference Simulation](https://mlsanity.com/paper/2109.02444/) (2021)
- [LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction](https://mlsanity.com/paper/2304.14343/) (2023)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [SLMRec: Distilling Large Language Models into Small for Sequential Recommendation](https://mlsanity.com/paper/2405.17890/) (2024)
- [ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation](https://mlsanity.com/paper/2502.13581/) (2025)
- [Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2212.08262/) (2022)
- [SelfCF: A Simple Framework for Self-supervised Collaborative Filtering](https://mlsanity.com/paper/2107.03019/) (2021)
- [Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation](https://mlsanity.com/paper/2501.14269/) (2025)
- [Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2407.11245/) (2024)
- [From Variability to Stability: Advancing RecSys Benchmarking Practices](https://mlsanity.com/paper/2402.09766/) (2024)
- [Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders](https://mlsanity.com/paper/2507.16289/) (2025)
- [RecDCL: Dual Contrastive Learning for Recommendation](https://mlsanity.com/paper/2401.15635/) (2024)
- [Slow Thinking for Sequential Recommendation](https://mlsanity.com/paper/2504.09627/) (2025)

## Abstract (arXiv)

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/.