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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 451–460 of 554 papers

TitleStatusHype
Generating Negative Samples for Sequential Recommendation—0
Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation—0
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential RecommendationCode0
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential RecommendationCode0
Recommender Transformers with Behavior Pathways—0
ID-Agnostic User Behavior Pre-training for Sequential Recommendation—0
Enhancing Sequential Recommendation with Graph Contrastive Learning—0
Personalized Prompt for Sequential Recommendation—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation—0
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