Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

AROMA

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

性行为放纵者

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

Commodious