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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 91100 of 554 papers

TitleStatusHype
Linear Recurrent Units for Sequential RecommendationCode1
KuaiSim: A Comprehensive Simulator for Recommender SystemsCode1
Diffusion Augmentation for Sequential RecommendationCode1
Leveraging Large Language Models for Sequential RecommendationCode1
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation LearningCode1
Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsCode1
Text Matching Improves Sequential Recommendation by Reducing Popularity BiasesCode1
LLMRec: Benchmarking Large Language Models on Recommendation TaskCode1
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for RecommendationCode1
Attention Calibration for Transformer-based Sequential RecommendationCode1
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