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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
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential RecommendationCode1
ELECRec: Training Sequential Recommenders as DiscriminatorsCode1
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k RecommendationCode1
Linear Recurrent Units for Sequential RecommendationCode1
SSD4Rec: A Structured State Space Duality Model for Efficient Sequential RecommendationCode1
Generative Recommender with End-to-End Learnable Item TokenizationCode1
A Survey on Cross-Domain Sequential RecommendationCode1
Linear-Time Self Attention with Codeword Histogram for Efficient RecommendationCode1
LinRec: Linear Attention Mechanism for Long-term Sequential Recommender SystemsCode1
Zero-Shot Next-Item Recommendation using Large Pretrained Language ModelsCode1
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