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Quaternion-Based Knowledge Graph Network for Recommendation
- Source :
- ACM Multimedia
- Publication Year :
- 2020
- Publisher :
- ACM, 2020.
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Abstract
- Recently, to alleviate the data sparsity and cold start problem, many research efforts have been devoted to the usage of knowledge graph (KG) in recommender systems. It is common for most existing KG based models to represent users and items using real-valued embeddings. However, compared with complex or hypercomplex numbers, these real-valued vectors are of less representation capacity and no intrinsic asymmetrical properties, thus may limit the modeling of interactions between entities and relations in KG. In this paper, we propose Quaternion-based Knowledge Graph Network (QKGN) for recommendation, which represents users and items with quaternion embeddings in hypercomplex space, so that the latent inter-dependencies between entities and relations could be captured effectively. In the core of our model, a semantic matching principle based on Hamilton product is applied to learn expressive quaternion representations from the unified user-item KG. On top of this, those embeddings are attentively updated by a customized preference propagation mechanism with structure information concerned. Finally, we apply the proposed QKGN to three real-world datasets of music, movie and book, and experimental results show the validity of our method.
- Subjects :
- Structure (mathematical logic)
Hypercomplex number
Theoretical computer science
Computer science
02 engineering and technology
Recommender system
020204 information systems
Core (graph theory)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Limit (mathematics)
Representation (mathematics)
Quaternion
Semantic matching
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 28th ACM International Conference on Multimedia
- Accession number :
- edsair.doi...........2ba655fe7c37af00e034b807f500d0fc