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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 121130 of 554 papers

TitleStatusHype
A Self-Correcting Sequential RecommenderCode1
Mutual Wasserstein Discrepancy Minimization for Sequential RecommendationCode1
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional TransformersCode1
Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential RecommendationCode1
Towards Out-of-Distribution Sequential Event Prediction: A Causal TreatmentCode1
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model GeneralizationCode1
Explanation Guided Contrastive Learning for Sequential RecommendationCode1
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed VideosCode1
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