The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Graph Contextualized Self-Attention Network for Session-based Recommendation
Open accessAbstract
Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming). Recently, Self-Attention Network (SAN) has achieved significant success in various sequence modeling tasks without using either recurrent or convolutional network. However, SAN lacks local dependencies that exist over adjacent items and limits its capacity for learning contextualized representations of items in sequences. In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation. In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN). Then each session learns long-range dependencies by applying the self-attention mechanism. Finally, each session is represented as a linear combination of the global preference and the current interest of that session. Extensive experiments on two real-world datasets show that GC-SAN outperforms state-of-the-art methods consistently.
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Cites
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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The graph neural network model
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Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
BPR: Bayesian Personalized Ranking from Implicit Feedback
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2618 · Cited by 4,242
Self-Attentive Sequential Recommendation
Kang, W.-C., & McAuley, J. (2018) · 2018 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2018.00035 · Cited by 3,011
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
A Structured Self-attentive Sentence Embedding
Lin, Z., Feng, M., Santos, C. N. D., Yu, M., Xiang, B., Zhou, B., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1703.03130 · Cited by 1,743
Neural Attentive Session-based Recommendation
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., & Ma, J. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management · doi:10.1145/3132847.3132926 · Cited by 1,501
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
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Improved Recurrent Neural Networks for Session-based Recommendations
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Self-citation
Cited by
RecBole: 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
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
A Comprehensive Survey on Deep Graph Representation Learning
Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., Qin, Y., Shen, J., Sun, F., Xiao, Z., et al. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106207 · Cited by 341
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
Zheng, B., Hou, Y., Lu, H., Chen, Y., Zhao, W. X., Chen, M., & Wen, J.-R. (2024) · 2024 IEEE 40th International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00118 · Cited by 143
COSMO: A Large-Scale E-commerce Common Sense Knowledge Generation and Serving System at Amazon
Yu, C., Liu, X., Maia, J., Li, Y., Cao, T., Gao, Y., Song, Y., Goutam, R., Zhang, H., Yin, B., et al. (2024) · Companion of the 2024 International Conference on Management of Data · doi:10.1145/3626246.3653398 · Cited by 13
Metadata
- Authors
- Xu, C., Zhao, P., Liu, Y., Sheng, V. S., Xu, J., Zhuang, F., Fang, J., & Zhou, X.
- Venue
- Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
- Published
- 2019-08
- Pages
- 3940-3946
- OpenAlex
- W2964926209
- Cited by
- 615
- References
- 21
- FWCI
- 52.21
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Xu, C., Zhao, P., Liu, Y., Sheng, V. S., Xu, J., Zhuang, F., Fang, J., & Zhou, X. (2019). Graph Contextualized Self-Attention Network for Session-based Recommendation. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/547
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