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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 191200 of 554 papers

TitleStatusHype
Similarity-Guided Diffusion for Contrastive Sequential Recommendation0
Unconditional Diffusion for Generative Sequential RecommendationCode0
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation0
A Systematic Replicability and Comparative Study of BSARec and SASRec for Sequential Recommendation0
C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized RecommendationCode0
Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain RecommendationCode0
LARES: Latent Reasoning for Sequential Recommendation0
HMamba: Hyperbolic Mamba for Sequential Recommendation0
M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation0
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation0
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