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 461470 of 554 papers

TitleStatusHype
Black-Box Attacks on Sequential Recommenders via Data-Free Model ExtractionCode1
Lightweight Self-Attentive Sequential Recommendation0
Is News Recommendation a Sequential Recommendation Task?0
Personalized next-best action recommendation with multi-party interaction learning for automated decision-making0
MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation0
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative TransformerCode1
Contrastive Self-supervised Sequential Recommendation with Robust AugmentationCode1
A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models0
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search0
Sequential Recommendation for Cold-start Users with Meta Transitional LearningCode1
Show:102550
← PrevPage 47 of 56Next →

No leaderboard results yet.