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HIN Recommendation via Graph Contrastive Learning by wf wf is a document available to read on EtoBox.
What is HIN Recommendation via Graph Contrastive Learning about?
The document presents a novel graph contrastive learning model, GCL-SS, for recommendation systems based on heterogeneous information networks (HIN), integrating both structural and semantic views to enhance user-item embeddings. The model employs a self-supervised contrastive learning mechanism and utilizes a time sequence encoder to strengthen semantic associations, achieving superior performance in rating prediction compared to existing methods. Experimental results on real datasets validate the effectiv
- Author
- wf wf
- Language
- EN