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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 271–280 of 554 papers

TitleStatusHype
Denoising Long- and Short-term Interests for Sequential Recommendation—0
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation—0
Denoising Self-attentive Sequential Recommendation—0
Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers—0
UPRec: User-Aware Pre-training for Recommender Systems—0
DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation—0
Time Lag Aware Sequential Recommendation—0
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems—0
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models—0
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models—0
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