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.
Recurrent neural networks with top-k gains for session-based recommendations
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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.