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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
Dynamic Graph Neural Networks for Sequential RecommendationCode1
E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential RecommendationCode1
Contrastive Learning for Representation Degeneration Problem in Sequential RecommendationCode1
Black-Box Attacks on Sequential Recommenders via Data-Free Model ExtractionCode1
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Contrastive Learning for Sequential RecommendationCode1
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential RecommendationCode1
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
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