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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 181190 of 554 papers

TitleStatusHype
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k RecommendationCode1
ELECRec: Training Sequential Recommenders as DiscriminatorsCode1
Modeling User Fatigue for Sequential RecommendationCode1
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential RecommendationCode1
Sequence-level Semantic Representation Fusion for Recommender SystemsCode1
Generative Recommender with End-to-End Learnable Item TokenizationCode1
A Survey on Cross-Domain Sequential RecommendationCode1
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
Frequency Enhanced Hybrid Attention Network for Sequential RecommendationCode1
Zero-Shot Next-Item Recommendation using Large Pretrained Language ModelsCode1
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