The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
Open accessAbstract
Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation.
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Distributed Representations of Words and Phrases and their Compositionality
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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
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Tang, J., & Wang, K. (2018) · Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining · doi:10.1145/3159652.3159656 · Cited by 1,975
Self-organization in a perceptual network
Linsker, R. (1988) · Computer · doi:10.1109/2.36 · Cited by 1,469
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
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Geng, S., Liu, S., Fu, Z., Ge, Y., & Zhang, Y. (2022) · Proceedings of the 16th ACM Conference on Recommender Systems · doi:10.1145/3523227.3546767 · Cited by 556
Machine Learning Methods for Small Data Challenges in Molecular Science
Dou, B., Zhu, Z., Merkurjev, E., Ke, L., Chen, L., Jiang, J., Zhu, Y., Liu, J., Zhang, B., & Wei, G.-W. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00189 · Cited by 499
Self-citationRecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
Zhao, W. X., Mu, S., Hou, Y., Lin, Z., Chen, Y., Pan, X., Li, K., Lu, Y., Wang, H., Tian, C., et al. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3482016 · Cited by 455
Self-citationSelf-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
Filter-enhanced MLP is All You Need for Sequential Recommendation
Zhou, K., Yu, H., Zhao, W. X., & Wen, J.-R. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512111 · Cited by 401
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Metadata
- Authors
- Zhou, K., Wang, H., Zhao, W. X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., & Wen, J.-R.
- Venue
- Proceedings of the 29th ACM International Conference on Information & Knowledge Management
- Publisher
- ACM
- Published
- 2020-10-19
- Pages
- 1893-1902
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W3065542300
- Cited by
- 789
- References
- 12
- FWCI
- 56.75
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhou, K., Wang, H., Zhao, W. X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., & Wen, J.-R. (2020). S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. Proceedings of the 29th ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/3340531.3411954
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