The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model
Abstract
Heterogeneous information network (HIN) has been widely adopted in recommender systems due to its excellence in modeling complex context information. Although existing HIN based recommendation methods have achieved performance improvement to some extent, they have two major shortcomings. First, these models seldom learn an explicit representation for path or meta-path in the recommendation task. Second, they do not consider the mutual effect between the meta-path and the involved user-item pair in an interaction. To address these issues, we develop a novel deep neural network with the co-attention mechanism for leveraging rich meta-path based context for top-N recommendation. We elaborately design a three-way neural interaction model by explicitly incorporating meta-path based context. To construct the meta-path based context, we propose to use a priority based sampling technique to select high-quality path instances. Our model is able to learn effective representations for users, items and meta-path based context for implementing a powerful interaction function. The co-attention mechanism improves the representations for meta-path based con- text, users and items in a mutual enhancement way. Extensive experiments on three real-world datasets have demonstrated the effectiveness of the proposed model. In particular, the proposed model performs well in the cold-start scenario and has potentially good interpretability for the recommendation results.
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Cites
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Cited by
Heterogeneous Graph Attention Network
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313562 · Cited by 3,191
Self-citationHeterogeneous Graph Neural Network
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Deep Learning Based Recommender System: A Survey and New Perspectives
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2020) · ACM Computing Surveys · doi:10.1145/3285029 · Cited by 1,511
Explainable Reasoning over Knowledge Graphs for Recommendation
Wang, X., Wang, D., Xu, C., He, X., Cao, Y., & Chua, T.-S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015329 · Cited by 785
Learning Intents behind Interactions with Knowledge Graph for Recommendation
Wang, X., Huang, T., Wang, D., Yuan, Y., Liu, Z., He, X., & Chua, T.-S. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3450133 · Cited by 578
Metadata
- Authors
- Hu, B., Shi, C., Zhao, W. X., & Yu, P. S.
- Venue
- Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2018-07-19
- Pages
- 1531-1540
- OpenAlex
- W2884134047
- Cited by
- 674
- References
- 43
- FWCI
- 52.22
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hu, B., Shi, C., Zhao, W. X., & Yu, P. S. (2018). Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3219819.3219965
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