ConvRec applies hierarchical convolutional layers to generate compact sequence representations for attribute-aware sequential recommendation, achieving linear complexity and outperforming attention-based state-of-the-art models on four real-world datasets.
Time interval aware self-attention for sequen- tial recommendation
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TME-PSR improves sequential recommendation accuracy and explanation quality by personalizing temporal rhythms, fine-grained interests, and recommendation-explanation alignment using a dual-view time encoder, multihead LRU, and dual-branch mutual information weighting.
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Rethinking Convolutional Networks for Attribute-Aware Sequential Recommendation
ConvRec applies hierarchical convolutional layers to generate compact sequence representations for attribute-aware sequential recommendation, achieving linear complexity and outperforming attention-based state-of-the-art models on four real-world datasets.
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TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation
TME-PSR improves sequential recommendation accuracy and explanation quality by personalizing temporal rhythms, fine-grained interests, and recommendation-explanation alignment using a dual-view time encoder, multihead LRU, and dual-branch mutual information weighting.