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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

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Showing 521–530 of 554 papers

TitleStatusHype
Learning to Structure Long-term Dependence for Sequential Recommendation—0
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation—0
SANST: A Self-Attentive Network for Next Point-of-Interest Recommendation—0
Memory Augmented Graph Neural Networks for Sequential RecommendationCode0
Seq2seq Translation Model for Sequential Recommendation—0
Sequential Recommendation with Relation-Aware Kernelized Self-Attention—0
Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation ModelingCode0
Cascading: Association Augmented Sequential Recommendation—0
Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations—0
SSE-PT: Sequential Recommendation Via Personalized TransformerCode0
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