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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 6170 of 554 papers

TitleStatusHype
Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsCode1
Explanation Guided Contrastive Learning for Sequential RecommendationCode1
DIFF: Dual Side-Information Filtering and Fusion for Sequential RecommendationCode1
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionCode1
Dynamic Graph Neural Networks for Sequential RecommendationCode1
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential RecommendationCode1
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Context-Aware Sequential Model for Multi-Behaviour RecommendationCode1
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative TransformerCode1
Collaborative Word-based Pre-trained Item Representation for Transferable RecommendationCode1
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