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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 341350 of 554 papers

TitleStatusHype
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training0
Sequential Recommendation with Diffusion Models0
Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation0
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
Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning0
Debiased Contrastive Learning for Sequential RecommendationCode1
Dually Enhanced Propensity Score Estimation in Sequential RecommendationCode1
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