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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 481–490 of 554 papers

TitleStatusHype
HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation—0
Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation—0
Sequential Movie Genre Prediction using Average Transition Probability with Clustering—0
HMamba: Hyperbolic Mamba for Sequential Recommendation—0
ID-Agnostic User Behavior Pre-training for Sequential Recommendation—0
ID-centric Pre-training for Recommendation—0
Intelligent Model Update Strategy for Sequential Recommendation—0
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation—0
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training—0
Image Fusion for Cross-Domain Sequential Recommendation—0
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