The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Session-Based Recommendation with Graph Neural Networks
Open accessAbstract
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.
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Cited by
Jin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G. I., King, I., & Pan, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3443141 · Cited by 567
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
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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
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
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Towards Universal Sequence Representation Learning for Recommender Systems
Hou, Y., Mu, S., Zhao, W. X., Li, Y., Ding, B., & Wen, J.-R. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539381 · Cited by 313
Metadata
- Authors
- Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 346-353
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2899457523
- Cited by
- 1,532
- References
- 25
- FWCI
- 110.64
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019). Session-Based Recommendation with Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v33i01.3301346
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