SOTAVerified

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

Showing 161170 of 554 papers

TitleStatusHype
Contrastive Self-supervised Sequential Recommendation with Robust AugmentationCode1
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative TransformerCode1
Sequential Recommendation for Cold-start Users with Meta Transitional LearningCode1
Position-enhanced and Time-aware Graph Convolutional Network for Sequential RecommendationsCode1
Sequential Recommendation with Graph Neural NetworksCode1
Modeling Sequences as Distributions with Uncertainty for Sequential RecommendationCode1
A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-SharingCode1
Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential RecommendationCode1
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionCode1
Linear-Time Self Attention with Codeword Histogram for Efficient RecommendationCode1
Show:102550
← PrevPage 17 of 56Next →

No leaderboard results yet.