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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 361370 of 554 papers

TitleStatusHype
Improving Sequential Recommendation Models with an Enhanced Loss FunctionCode0
Local Policy Improvement for Recommender Systems0
Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential RecommendationCode1
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
Towards Out-of-Distribution Sequential Event Prediction: A Causal TreatmentCode1
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