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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2019 2verdicts
UNVERDICTED 2representative citing papers
A novel framework jointly captures flat and hierarchical side information in recommender systems and shows significant performance gains over state-of-the-art methods on real-world datasets.
citing papers explorer
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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.
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Recommender Systems with Heterogeneous Side Information
A novel framework jointly captures flat and hierarchical side information in recommender systems and shows significant performance gains over state-of-the-art methods on real-world datasets.