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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 6170 of 554 papers

TitleStatusHype
Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsCode1
Explanation Guided Contrastive Learning for Sequential RecommendationCode1
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional TransformersCode1
DiffuRec: A Diffusion Model for Sequential RecommendationCode1
Dually Enhanced Propensity Score Estimation in Sequential RecommendationCode1
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential RecommendationCode1
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential RecommendationCode1
Controllable Multi-Interest Framework for RecommendationCode1
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
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