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Entity Embeddings

Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.

Papers

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Showing 141–150 of 151 papers

TitleStatusHype
Graph Neural Pre-training for Enhancing Recommendations using Side Information—0
Graph Reasoning for Explainable Cold Start Recommendation—0
Embedding Knowledge Graphs in Degenerate Clifford Algebras—0
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings—0
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users—0
Improving Entity Linking by Encoding Type Information into Entity Embeddings—0
Improving Entity Linking by Modeling Latent Entity Type Information—0
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph—0
Improving Question Answering over Knowledge Graphs Using Graph Summarization—0
Social World Knowledge: Modeling and Applications—0
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