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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
AutoSeqRec: Autoencoder for Efficient Sequential RecommendationCode0
MV-RNN: A Multi-View Recurrent Neural Network for Sequential RecommendationCode0
C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized RecommendationCode0
Neighborhood-based Hard Negative Mining for Sequential RecommendationCode0
CSSR: A Context-Aware Sequential Software Service Recommendation ModelCode0
Your Causal Self-Attentive Recommender Hosts a Lonely NeighborhoodCode0
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential RecommendationCode0
CSRec: Rethinking Sequential Recommendation from A Causal PerspectiveCode0
GenRec: Generative Sequential Recommendation with Large Language ModelsCode0
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential RecommendationCode0
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