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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A unified, comprehensive and efficient recommendation library
| Date | Stars |
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| 2026-07-24 | 4522 |
| 2026-07-25 | 4522 |
| 2026-07-28 | 4522 |
| 2026-07-30 | 4522 |
| 2026-08-06 | 4522 |
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 -------------------------------------------------------------------------------- # RecBole (伯乐) *“世有伯乐，然后有千里马。千里马常有，而伯乐不常有。”——韩愈《马说》* [](https://pypi.org/project/recbole/) [](https://anaconda.org/aibox/recbole) [](./LICENSE) [](https://arxiv.org/abs/2011.01731) [HomePage] | [Docs] | [Datasets] | [Paper] | [Blogs] | [Models] | [中文版] [HomePage]: https://recbole.io/ [Docs]: https://recbole.io/docs/ [Datasets]: https://github.com/RUCAIBox/RecDatasets [Paper]: https://arxiv.org/abs/2011.01731 [Blogs]: https://blog.csdn.net/Turinger_2000/article/details/111182852 [Models]: https://github.com/RUCAIBox/RecBole2.0/blob/main/model_list.md [中文版]: README_CN.md RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose. Our library includes 94 recommendation algorithms, covering four major categories: + General Recommendation + Sequential Recommendation + Context-aware Recommendation + Knowledge-based Recommendation We design a unified and flexible data file format, and provide the support for 44 benchmark recommendation datasets. A user can apply the provided script to process the original data copy, or simply download the processed datasets by our team. <p align="center"> <img src="asset/framework.png" alt="RecBole v0.1 architecture" width="600"> <br> <b>Figure</b>: RecBole Overall Architecture </p> In order to support the study of recent advances in recommender systems, we construct an extended recommendation library [RecBole2.0](https://github.com/RUCAIBox/RecBole2.0) consisting of 8 packages for up-to-date topics and architectures (e.g., debiased, fairness and GNNs). ## Feature + **General and extensible data structure.** We design general and extensible data structures to unify the formatting and usage of various recommendation datasets. + **Comprehensive benchmark models and datasets.** We implement 94 commonly used recommendation algorithms, and provide the formatted copies of 44 recommendation datasets. + **Efficient GPU-accelerated execution.** We optimize the efficiency of our library with a number of improved techniques oriented to the GPU environment. + **Extensive and standard evaluation protocols.** We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. ## RecBole News  **02/23/2025**: We release RecBole [v1.2.1](https://github.com/RUCAIBox/RecBole/releases/tag/v1.2.1).  **11/01/2023**: We release RecBole [v1.2.0](https://github.com/RUCAIBox/RecBole/releases/tag/v1.2.0). **11/06/2022**: We release [the optimal hyperparameters of the model and their tuning ranges](https://recbole.io/hyperparameters/index.html). **10/05/2022**: We release RecBole [v1.1.1](https://github.com/RUCAIBox/RecBole/releases/tag/v1.1.1). **06/28/2022**: We release [**RecBole2.0**](https://github.com/RUCAIBox/RecBole2.0) with **8 packages** consisting of **65 newly implement models**. **02/25/2022**: We release RecBole [v1.0.1](https://github.com/RUCAIBox/RecBole/releases/tag/v1.0.1). **09/17/2021**: We release RecBole [v1.0.0](https://github.com/RUCAIBox/RecBole/releases/tag/v1.0.0). **03/22/2021**: We release RecBole [v0.2.1](https://github.com/RUCAIBox/RecBole/releases/tag/v0.2.1). **01/15/2021**: We release RecBole [v0.2.0](https://github.com/RUCAIBox/RecBole/releases/tag/v0.2.0). **12/10/2020**: 我们发布了[RecBole小白入门系列中文博客（持续更新中）](https://blog.csdn.net/Turinger_2000/article/details/111182852) 。 **12/06/2020**: We releas
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chenyushuo
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Yupeng Hou · Google DeepMind
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Bowen Zheng
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:90916739ea7bfe79, topic:deep-learning, topic:pytorch
matched fp:90916739ea7bfe79, topic:knowledge-graph