The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Heterogeneous Information Network Embedding for Recommendation
Open accessAbstract
Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, calledHIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.
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Cites
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Structural Deep Network Embedding
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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-citationSelf-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning
Wang, X., Liu, N., Han, H., & Shi, C. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467415 · Cited by 467
Self-citationA Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., & Yu, P. S. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3177455 · Cited by 438
Self-citationLearning Graph Embedding With Adversarial Training Methods
Pan, S., Hu, R., Fung, S.-F., Long, G., Jiang, J., & Zhang, C. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2932096 · Cited by 363
Representation learning for dynamic graphs: A survey
Kazemi, S. M., Goel, R., Jain, K., Kobyzev, I., Sethi, A., Forsyth, P., & Poupart, P. (2019) · arXiv · doi:10.48550/arxiv.1905.11485 · Cited by 338
Metadata
- Authors
- Shi, C., Hu, B., Zhao, W. X., & Yu, P. S.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2019-02-01
- Volume
- 31
- Issue
- 2
- Pages
- 357-370
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W2963707260
- Cited by
- 1,204
- References
- 57
- FWCI
- 188.01
- Type
- journal article
- Language
- en
- Open access
- green
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- Shi, C., Hu, B., Zhao, W. X., & Yu, P. S. (2019). Heterogeneous Information Network Embedding for Recommendation. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2018.2833443
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