A framework learns collection embeddings from runway images and applies RNN/LSTM to predict next-season designs at 78.42% average AUC over 32 years of data.
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UNVERDICTED 2representative citing papers
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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Predicting Next-Season Designs on High Fashion Runway
A framework learns collection embeddings from runway images and applies RNN/LSTM to predict next-season designs at 78.42% average AUC over 32 years of data.
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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.