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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 171180 of 554 papers

TitleStatusHype
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training TransformerCode1
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationCode1
Dynamic Graph Neural Networks for Sequential RecommendationCode1
Adversarial and Contrastive Variational Autoencoder for Sequential RecommendationCode1
Dynamic Memory based Attention Network for Sequential RecommendationCode1
Sparse-Interest Network for Sequential RecommendationCode1
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential RecommendationCode1
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative StackingCode1
Mixed Information Flow for Cross-domain Sequential RecommendationsCode1
Future-Aware Diverse Trends Framework for RecommendationCode1
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