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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 141150 of 554 papers

TitleStatusHype
Diffusion-based Contrastive Learning for Sequential RecommendationCode1
Dynamic Graph Neural Networks for Sequential RecommendationCode1
ELECRec: Training Sequential Recommenders as DiscriminatorsCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
SIGMA: Selective Gated Mamba for Sequential RecommendationCode1
DIFF: Dual Side-Information Filtering and Fusion for Sequential RecommendationCode1
DiffuRec: A Diffusion Model for Sequential RecommendationCode1
Diffusion Augmentation for Sequential RecommendationCode1
Leveraging Large Language Models for Sequential RecommendationCode1
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