The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Learning Intents behind Interactions with Knowledge Graph for Recommendation
Open accessAbstract
Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity.
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Cites
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A simple framework for contrastive learning of visual representations
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Inductive Representation Learning on Large Graphs
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Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
Silva, G., Rodrigues, M., Teixeira, A., & Amorim, M. (2024) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.sea.2024.20 · Cited by 5,183
Cited by
Disentangled Contrastive Collaborative Filtering
Ren, X., Xia, L., Zhao, J., Yin, D., & Huang, C. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591665 · Cited by 171
Role-Augmented Intent-Driven Generative Search Engine Optimization
Chen, X., Bao, J., Wu, H., Chen, Z., Pan, L., & Liao, Y. (2025) · arXiv · doi:10.48550/arxiv.2508.11158
“Recent advances in prompt engineering have enabled LLMs to make significant progress in intent understanding [16], [25], [26], [31].”
Metadata
- Authors
- Wang, X., Huang, T., Wang, D., Yuan, Y., Liu, Z., He, X., & Chua, T.-S.
- Venue
- Proceedings of the Web Conference 2021
- Publisher
- ACM
- Published
- 2021-04-19
- Pages
- 878-887
- OpenAlex
- W3129482887
- Cited by
- 578
- References
- 59
- FWCI
- 74.57
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Wang, X., Huang, T., Wang, D., Yuan, Y., Liu, Z., He, X., & Chua, T.-S. (2021). Learning Intents behind Interactions with Knowledge Graph for Recommendation. Proceedings of the Web Conference 2021. https://doi.org/10.1145/3442381.3450133
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