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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 441–450 of 554 papers

TitleStatusHype
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—0
SANST: A Self-Attentive Network for Next Point-of-Interest Recommendation—0
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers—0
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation—0
Towards commands recommender system in BIM authoring tool using transformers—0
Scaling Law of Large Sequential Recommendation Models—0
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation—0
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search—0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
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