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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 151160 of 554 papers

TitleStatusHype
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Mixed Attention Network for Cross-domain Sequential RecommendationCode1
Mixed Information Flow for Cross-domain Sequential RecommendationsCode1
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionCode1
Filter-enhanced MLP is All You Need for Sequential RecommendationCode1
Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential RecommendationCode1
Exploiting Session Information in BERT-based Session-aware Sequential RecommendationCode1
LLaRA: Large Language-Recommendation AssistantCode1
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential RecommendationCode1
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