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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 401410 of 554 papers

TitleStatusHype
Multi-Behavior Sequential Recommendation with Temporal Graph TransformerCode1
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual ActorCode1
Enhancing Sequential Recommendation with Graph Contrastive Learning0
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential RecommendationCode1
Personalized Prompt for Sequential Recommendation0
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential RecommendationCode1
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation0
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation0
Selective Fairness in Recommendation via PromptsCode1
When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential RecommendationCode1
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