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.
In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence
2 Pith papers cite this work. Polarity classification is still indexing.
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Applies Information Foraging Theory to demonstrate that visual bookmarks increase the scent of recommended images in a content-based image recommender evaluated on Pinterest.
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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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Effects of Foraging in Personalized Content-based Image Recommendation
Applies Information Foraging Theory to demonstrate that visual bookmarks increase the scent of recommended images in a content-based image recommender evaluated on Pinterest.