The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
Open accessAbstract
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'[email protected]]), 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/.
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Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512
Neural Collaborative Filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052569 · Cited by 6,652
LINE: Large-scale Information Network Embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401063 · Cited by 4,430
Self-citationNeural Graph Collaborative Filtering
Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331267 · Cited by 3,823
Self-citationWide & Deep Learning for Recommender Systems
Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., et al. (2016) · Proceedings of the 1st Workshop on Deep Learning for Recommender Systems · doi:10.1145/2988450.2988454 · Cited by 3,535
Rendle, S. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.127 · Cited by 3,189
Factorizing personalized Markov chains for next-basket recommendation
Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772773 · Cited by 2,692
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3357895 · Cited by 2,558
Cited by
Self-supervised learning for recommender systems: A survey
Yu, J., Yin, H., Xia, X., Chen, T., Li, J., & Huang, Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3282907 · Cited by 413
Large Language Models are Zero-Shot Rankers for Recommender Systems
Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., & Zhao, W. X. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-56060-6_24 · Cited by 327
Self-citationUnified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
Deng, Y., Li, Y., Sun, F., Ding, B., & Lam, W. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462913 · Cited by 136
Self-citationAgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
Zhang, J., Hou, Y., Xie, R., Sun, W., McAuley, J., Zhao, W. X., Lin, L., & Wen, J.-R. (2024) · Proceedings of the ACM Web Conference 2024 · doi:10.1145/3589334.3645537 · Cited by 94
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Metadata
- Authors
- Zhao, W. X., Mu, S., Hou, Y., Lin, Z., Chen, Y., Pan, X., Li, K., Lu, Y., Wang, H., Tian, C., Min, Y., Feng, Z., Fan, X., Chen, X., Wang, P., Ji, W., Li, Y., Wang, X., & Wen, J.-R.
- Venue
- Proceedings of the 30th ACM International Conference on Information & Knowledge Management
- Publisher
- ACM
- Published
- 2021-10-26
- Pages
- 4653-4664
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W3208227120
- Cited by
- 455
- References
- 69
- FWCI
- 60.14
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhao, W. X., Mu, S., Hou, Y., Lin, Z., Chen, Y., Pan, X., Li, K., Lu, Y., Wang, H., Tian, C., Min, Y., Feng, Z., Fan, X., Chen, X., Wang, P., Ji, W., Li, Y., Wang, X., & Wen, J.-R. (2021). RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/3459637.3482016
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