ConvNCF improves neural collaborative filtering by explicitly modeling pairwise correlations via outer product and high-order correlations via CNN on user-item embeddings.
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Pith papers citing it
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cs.IR 3years
2019 3verdicts
UNVERDICTED 3representative citing papers
NPA applies CNN-based news encoding and personalized attention (word- and news-level) driven by user ID embeddings to improve click prediction on an MSN news dataset.
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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Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
ConvNCF improves neural collaborative filtering by explicitly modeling pairwise correlations via outer product and high-order correlations via CNN on user-item embeddings.
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NPA: Neural News Recommendation with Personalized Attention
NPA applies CNN-based news encoding and personalized attention (word- and news-level) driven by user ID embeddings to improve click prediction on an MSN news dataset.
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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.