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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 171180 of 554 papers

TitleStatusHype
EAGER: Two-Stream Generative Recommender with Behavior-Semantic CollaborationCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential RecommendationCode1
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential RecommendationCode1
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed VideosCode1
Meta-optimized Contrastive Learning for Sequential RecommendationCode1
Rethinking Cross-Domain Sequential Recommendation under Open-World AssumptionsCode1
Efficient Failure Pattern Identification of Predictive AlgorithmsCode1
RecGPT: Generative Pre-training for Text-based RecommendationCode1
Towards Out-of-Distribution Sequential Event Prediction: A Causal TreatmentCode1
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