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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 161170 of 554 papers

TitleStatusHype
Exploiting Session Information in BERT-based Session-aware Sequential RecommendationCode1
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential RecommendationCode1
Generative Recommender with End-to-End Learnable Item TokenizationCode1
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for RecommendationCode1
Dually Enhanced Propensity Score Estimation in Sequential RecommendationCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
Intent Contrastive Learning for Sequential RecommendationCode1
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential RecommendationCode1
Dynamic Memory based Attention Network for Sequential RecommendationCode1
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain RecommendationCode1
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