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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–460 of 554 papers

TitleStatusHype
Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-CommerceCode1
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
Learning to Learn a Cold-start Sequential Recommender—0
Contrastive Learning for Representation Degeneration Problem in Sequential RecommendationCode1
Self-supervised Learning for Sequential Recommendation with Model Augmentation—0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential RecommendationCode1
A Survey on Reinforcement Learning for Recommender Systems—0
CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation—0
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential RecommendationCode1
Show:102550
← PrevPage 46 of 56Next →

No leaderboard results yet.