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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 431440 of 554 papers

TitleStatusHype
Local Policy Improvement for Recommender Systems0
Denoising Self-attentive Sequential Recommendation0
Equivariant Contrastive Learning for Sequential RecommendationCode0
One Person, One Model--Learning Compound Router for Sequential RecommendationCode0
Self-Attentive Sequential Recommendation with Cheap Causal Convolutions0
Disentangling Past-Future Modeling in Sequential Recommendation via Dual NetworksCode0
Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational TransformerCode0
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems0
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation0
Mutual Harmony: Sequential Recommendation with Dual Contrastive NetworkCode0
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