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 261–270 of 554 papers

TitleStatusHype
Learning to Structure Long-term Dependence for Sequential Recommendation—0
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
Coarse-to-Fine Sparse Sequential Recommendation—0
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—0
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning—0
CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation—0
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation—0
ELASTIC: Efficient Linear Attention for Sequential Interest Compression—0
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning—0
CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation—0
Show:102550
← PrevPage 27 of 56Next →

No leaderboard results yet.