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 211220 of 554 papers

TitleStatusHype
Personalized Top-N Sequential Recommendation via Convolutional Sequence EmbeddingCode0
Contrastive Enhanced Slide Filter Mixer for Sequential RecommendationCode0
A Hierarchical Contextual Attention-based GRU Network for Sequential RecommendationCode0
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial ActionsCode0
Prediction then Correction: An Abductive Prediction Correction Method for Sequential RecommendationCode0
MV-RNN: A Multi-View Recurrent Neural Network for Sequential RecommendationCode0
Neighborhood-based Hard Negative Mining for Sequential RecommendationCode0
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential RecommendationCode0
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential RecommendationCode0
One Person, One Model--Learning Compound Router for Sequential RecommendationCode0
Show:102550
← PrevPage 22 of 56Next →

No leaderboard results yet.