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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 111120 of 554 papers

TitleStatusHype
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential RecommendationCode1
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
Controllable Multi-Interest Framework for RecommendationCode1
Attention Mixtures for Time-Aware Sequential RecommendationCode1
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training TransformerCode1
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential RecommendationCode1
Augmenting Sequential Recommendation with Balanced Relevance and DiversityCode1
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model GeneralizationCode1
AlphaFuse: Learn ID Embeddings for Sequential Recommendation in Null Space of Language EmbeddingsCode1
Graph Masked Autoencoder for Sequential RecommendationCode1
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