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Sequential Recommendation

Sequential recommendation is a sophisticated approach to providing personalized suggestions by analyzing users' historical interactions in a sequential manner. Unlike traditional recommendation systems, which consider items in isolation, sequential recommendation takes into account the temporal order of user actions. This method is particularly valuable in domains where the sequence of events matters, such as streaming services, e-commerce platforms, and social media.

Papers

Showing 261270 of 554 papers

TitleStatusHype
One Person, One Model--Learning Compound Router for Sequential RecommendationCode0
Privacy-Preserving Cross-Domain Sequential RecommendationCode0
Breaking the Clusters: Uniformity-Optimization for Text-Based Sequential RecommendationCode0
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential RecommendationCode0
Disentangling Past-Future Modeling in Sequential Recommendation via Dual NetworksCode0
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
Memory Augmented Graph Neural Networks for Sequential RecommendationCode0
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item PredictionCode0
MaTrRec: Uniting Mamba and Transformer for Sequential RecommendationCode0
Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential RecommendationCode0
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