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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 551554 of 554 papers

TitleStatusHype
TRec: Sequential Recommender Based On Latent Item Trend Information0
SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation0
MLSA4Rec: Mamba Combined with Low-Rank Decomposed Self-Attention for Sequential Recommendation0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation0
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