The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Recurrent knowledge graph embedding for effective recommendation
Open accessAbstract
Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.
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Cites
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Neural Collaborative Filtering
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Cited by
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
Self-citationZhang, Y., Qian, S., Fang, Q., & Xu, C. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3351033 · Cited by 33
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Metadata
- Authors
- Sun, Z., Yang, J., Zhang, J., Bozzon, A., Huang, L.-K., & Xu, C.
- Venue
- Proceedings of the 12th ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2018-09-27
- Pages
- 297-305
- OpenAlex
- W2893775232
- Cited by
- 367
- References
- 38
- FWCI
- 25.31
- Type
- conference paper
- Language
- en
- Open access
- green
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