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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 541550 of 554 papers

TitleStatusHype
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender SystemCode0
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential RecommendationCode0
Learning Robust Sequential Recommenders through Confident Soft LabelsCode0
Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation ModelingCode0
DeepRec: An Open-source Toolkit for Deep Learning based RecommendationCode0
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential RecommendationCode0
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential RecommendationCode0
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential RecommendationCode0
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial ActionsCode0
AutoSAM: Towards Automatic Sampling of User Behaviors for Sequential Recommender SystemsCode0
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