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 371380 of 554 papers

TitleStatusHype
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems0
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation0
Mutual Harmony: Sequential Recommendation with Dual Contrastive NetworkCode0
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial ActionsCode0
Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation0
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model GeneralizationCode1
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential RecommendationCode0
Explanation Guided Contrastive Learning for Sequential RecommendationCode1
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
Show:102550
← PrevPage 38 of 56Next →

No leaderboard results yet.