The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks
Abstract
Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the two problems by first introducing the concept of meta-graph to HIN-based recommendation, and then solving the information fusion problem with a "matrix factorization (MF) + factorization machine (FM)" approach. For the similarities generated by each meta-graph, we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to state-of-the-art FM and other HIN-based recommendation algorithms.
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Cites
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Cited by
Heterogeneous Information Network Embedding for Recommendation
Shi, C., Hu, B., Zhao, W. X., & Yu, P. S. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2833443 · Cited by 1,204
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., & Guo, M. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271739 · Cited by 1,160
Knowledge Graph Convolutional Networks for Recommender Systems
Wang, H., Zhao, M., Xie, X., Li, W., & Guo, M. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313417 · Cited by 1,021
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
Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model
Hu, B., Shi, C., Zhao, W. X., & Yu, P. S. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219965 · Cited by 674
Metadata
- Authors
- Zhao, H., Yao, Q., Li, J., Song, Y., & Lee, D. L.
- Venue
- Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2017-08-04
- Pages
- 635-644
- OpenAlex
- W2743159750
- Cited by
- 565
- References
- 38
- FWCI
- 36.00
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhao, H., Yao, Q., Li, J., Song, Y., & Lee, D. L. (2017). Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3097983.3098063
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