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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 4150 of 554 papers

TitleStatusHype
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Attention Mixtures for Time-Aware Sequential RecommendationCode1
Advances in Collaborative Filtering and RankingCode1
Contrastive Self-supervised Sequential Recommendation with Robust AugmentationCode1
A Self-Correcting Sequential RecommenderCode1
A Survey on Cross-Domain Sequential RecommendationCode1
A Systematic Review and Replicability Study of BERT4Rec for Sequential RecommendationCode1
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationCode1
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training TransformerCode1
Contrastive Learning for Sequential RecommendationCode1
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