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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 111120 of 554 papers

TitleStatusHype
Frequency Enhanced Hybrid Attention Network for Sequential RecommendationCode1
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential RecommendationCode1
Attention Mixtures for Time-Aware Sequential RecommendationCode1
Meta-optimized Contrastive Learning for Sequential RecommendationCode1
Contrastive Cross-Domain Sequential RecommendationCode1
Zero-Shot Next-Item Recommendation using Large Pretrained Language ModelsCode1
DiffuRec: A Diffusion Model for Sequential RecommendationCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Debiased Contrastive Learning for Sequential RecommendationCode1
Dually Enhanced Propensity Score Estimation in Sequential RecommendationCode1
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