| 引用本文: | 王晨阳,任一,马为之,张敏,刘奕群,马少平.ReChorus: 综合高效易扩展的轻量级推荐算法框架.软件学报,2022,33(4):1430-1438 |
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| ReChorus: 综合高效易扩展的轻量级推荐算法框架 |
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王晨阳1,2, 任一1,2, 马为之1,2, 张敏1,2, 刘奕群1,2, 马少平1,2
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1.清华大学计算机科学与技术系, 北京 100084;2.北京信息科学与技术国家研究中心 清华大学, 北京 100084
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| 摘要: |
| 近年来,各种各样的推荐算法层出不穷,特别是深度学习的发展,极大地推动了推荐系统的研究.然而,各个推荐算法在实现细节、评价方式、数据集处理等方面存在众多差异,越来越多的研究者开始对推荐领域的可复现性产生担忧.为了帮助缓解上述问题,基于PyTorch实现了一个综合、高效、易扩展的轻量级推荐算法框架ReChorus,意为构建一个推荐算法的“合唱团”.ReChorus框架中实现了多种不同类型的推荐算法,类别涵盖常规推荐、序列推荐、引入知识图谱的推荐、引入时间动态性的推荐等;同时,对于一些常见的数据集也提供统一的预处理范式.相比其他推荐系统库,ReChorus在保证综合高效的基础上尽可能做到了轻量实用,同时具有较高的可扩展性,尤其以方便学术研究为导向,非常容易上手实现新的模型.不同的推荐算法在ReChorus框架中能够在相同的实验设定下进行训练和评测,从而实现推荐算法间的有效对比.该项目目前已在GitHub发布:https://github.com/THUwangcy/ReChorus. |
| 关键词: 推荐系统 深度学习 可复现性 推荐算法框架 软件工具包 |
| DOI：10.13328/j.cnki.jos.006473 |
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| 基金项目:国家重点研发计划(2018YFC0831900);国家自然科学基金(61672311,61532011,62002191);清华大学国强研究院资助 |
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| ReChorus: Comprehensive, Efficient, Flexible, and Lightweight Framework for Recommendation Algorithms |
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WANG Chen-Yang1,2, REN Yi1,2, MA Wei-Zhi1,2, ZHANG Min1,2, LIU Yi-Qun1,2, MA Shao-Ping1,2
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1.Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;2.Beijing National Research Center for Information Science and Technology Tsinghua University, Beijing 100084, China
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| Abstract: |
| In recent years, many recommendation algorithms have been proposed, and the research of recommender system has been greatly boosted with the development of deep learning. However, concerns about the reproducibility in this field have increasingly arisen in the research community, owing to the slight but influential differences between recommendation algorithms, such as implementation details, evaluation protocols, dataset splitting, etc. To address this issue, ReChorus is presented, of which it is a comprehensive, efficient, flexible, and lightweight framework for recommendation algorithms based on PyTorch, with aims to form a “Chorus” of recommendation algorithms. In this framework, a wide range of recommendation algorithms of different categories is implemented, covering general recommendation, sequential recommendation, knowledge-aware recommendation, and time-aware recommendation. ReChorus also provides the paradigm of dataset preprocessing for some common datasets. Compared to other recommendation algorithm libraries, ReChorus is featured for that it strives to keep lightweight while unifies as many as different algorithms at the same time. ReChorus is also flexible, efficient, and easy to use, especially for research purposes. Researchers will find it effortless to implement new algorithms with ReChorus. Such a framework can help to train and evaluate different recommendation models under the same experimental setting, so as to avoid the impacts resulting from implementation details and assure an effective comparison among recommendation algorithms. The project has been released on GitHub: https://github.com/THUwangcy/ReChorus. |
| Key words: recommender system deep learning reproducibility recommendation algorithm framework software toolkit |
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