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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 501–510 of 554 papers

TitleStatusHype
Future-Aware Diverse Trends Framework for RecommendationCode1
Contrastive Learning for Sequential RecommendationCode1
XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender SystemCode0
TRec: Sequential Recommender Based On Latent Item Trend Information—0
MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential RecommendationCode1
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information MaximizationCode1
Sequential recommendation with metric models based on frequent sequences—0
Learning Post-Hoc Causal Explanations for Recommendation—0
Self-Supervised Reinforcement Learning for Recommender Systems—0
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective—0
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