AROMA
发表于 2025-3-30 08:38:28
Multi-intent Aware Contrastive Learning for Sequential Recommendationare contrastive learning strategy to mitigate the impact of pair-wise representations with high similarity. Experimental results on widely used four datasets demonstrate the effectiveness of our method for sequential recommendation.
V切开
发表于 2025-3-30 16:11:05
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性行为放纵者
发表于 2025-3-30 17:01:00
Time-Aware Squeeze-Excitation Transformer for Sequential Recommendationation Attention (sigmoid activation) to comprehensively capture relevant items, thus enhancing prediction accuracy. Extensive experiments validate the superiority of the proposed model over various state-of-the-art models under several widely used evaluation metrics.
MAL
发表于 2025-3-31 00:01:09
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Commodious
发表于 2025-3-31 03:20:44
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