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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 101110 of 554 papers

TitleStatusHype
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
Contrastive Self-supervised Sequential Recommendation with Robust AugmentationCode1
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative TransformerCode1
Contrastive Cross-Domain Sequential RecommendationCode1
Diffusion Augmentation for Sequential RecommendationCode1
Contrastive Learning for Representation Degeneration Problem in Sequential RecommendationCode1
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment RecommendationCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Contrastive Learning for Sequential RecommendationCode1
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionCode1
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