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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 7180 of 554 papers

TitleStatusHype
Debiased Contrastive Learning for Sequential RecommendationCode1
SIGMA: Selective Gated Mamba for Sequential RecommendationCode1
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential RecommendationCode1
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System ExposureCode1
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional TransformersCode1
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential RecommendationCode1
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
Diffusion Augmentation for Sequential RecommendationCode1
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
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