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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
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
GLoSS: Generative Language Models with Semantic Search for Sequential RecommendationCode1
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
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
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation LearningCode1
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