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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 141150 of 554 papers

TitleStatusHype
Selective Fairness in Recommendation via PromptsCode1
When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential RecommendationCode1
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
Decoupled Side Information Fusion for Sequential RecommendationCode1
Exploiting Session Information in BERT-based Session-aware Sequential RecommendationCode1
ELECRec: Training Sequential Recommenders as DiscriminatorsCode1
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-AttentionCode1
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential RecommendationCode1
Improving Contrastive Learning with Model AugmentationCode1
Filter-enhanced MLP is All You Need for Sequential RecommendationCode1
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