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 481490 of 554 papers

TitleStatusHype
Sparse Attentive Memory Network for Click-through Rate Prediction with Long SequencesCode0
C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized RecommendationCode0
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement LearningCode0
Mutual Harmony: Sequential Recommendation with Dual Contrastive NetworkCode0
HAM: Hybrid Associations Models for Sequential RecommendationCode0
Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution NetworkCode0
SS4Rec: Continuous-Time Sequential Recommendation with State Space ModelsCode0
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential RecommendationCode0
Enhancing Sequential Music Recommendation with Personalized Popularity AwarenessCode0
Hierarchical Context enabled Recurrent Neural Network for RecommendationCode0
Show:102550
← PrevPage 49 of 56Next →

No leaderboard results yet.