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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 361–370 of 554 papers

TitleStatusHype
A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot TransferCode0
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential RecommendationCode0
A novel diffusion recommendation algorithm based on multi-scale cnn and residual lstm—0
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation—0
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training—0
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
Scaling Law of Large Sequential Recommendation Models—0
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation—0
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising ApproachCode0
Learning Robust Sequential Recommenders through Confident Soft LabelsCode0
Show:102550
← PrevPage 37 of 56Next →

No leaderboard results yet.