Fair re-ranking is equivalent to gradient descent on a ranking manifold under Walrasian equilibrium in an attention market, yielding the ManifoldRank algorithm that adjusts gradients for supply-side fairness costs and demand-side score predictions.
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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.
HGN introduces feature-level and instance-level gating plus explicit item-item products to capture long- and short-term interests for improved top-N sequential recommendation on implicit feedback.
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.
citing papers explorer
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The Attention Market: Interpreting Online Fair Re-ranking as Manifold Optimization under Walrasian Equilibrium
Fair re-ranking is equivalent to gradient descent on a ranking manifold under Walrasian equilibrium in an attention market, yielding the ManifoldRank algorithm that adjusts gradients for supply-side fairness costs and demand-side score predictions.
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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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Hierarchical Gating Networks for Sequential Recommendation
HGN introduces feature-level and instance-level gating plus explicit item-item products to capture long- and short-term interests for improved top-N sequential recommendation on implicit feedback.
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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.