Proposes Adaptive Margin Loss (AML) for TransE-style KG embeddings that uses a correntropy objective to adaptively expand the margin during training, requiring only a single center value instead of upper/lower bounds.
In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining
2 Pith papers cite this work. Polarity classification is still indexing.
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2019 2verdicts
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NeuCDCF is a wide-and-deep neural architecture for cross-domain collaborative filtering that jointly learns matrix factorization and deep representations, reporting better performance than prior CDCF models on four real-world datasets.
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Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function
Proposes Adaptive Margin Loss (AML) for TransE-style KG embeddings that uses a correntropy objective to adaptively expand the margin during training, requiring only a single center value instead of upper/lower bounds.
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Neural Cross-Domain Collaborative Filtering with Shared Entities
NeuCDCF is a wide-and-deep neural architecture for cross-domain collaborative filtering that jointly learns matrix factorization and deep representations, reporting better performance than prior CDCF models on four real-world datasets.