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
Graph Masked Autoencoder for Sequential RecommendationCode1
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative SamplingCode1
Harnessing Large Language Models for Text-Rich Sequential RecommendationCode1
Preference Diffusion for RecommendationCode1
Dually Enhanced Propensity Score Estimation in Sequential RecommendationCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential RecommendationCode1
Dynamic Graph Neural Networks for Sequential RecommendationCode1
Plug-in Diffusion Model for Sequential RecommendationCode1
Rethinking Cross-Domain Sequential Recommendation under Open-World AssumptionsCode1
Show:102550
← PrevPage 17 of 56Next →

No leaderboard results yet.