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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 131140 of 554 papers

TitleStatusHype
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
A Systematic Review and Replicability Study of BERT4Rec for Sequential RecommendationCode1
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential RecommendationCode1
Effective and Efficient Training for Sequential Recommendation using Recency SamplingCode1
Sequential Recommendation Model for Next Purchase PredictionCode1
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential RecommendationCode1
Multi-Behavior Sequential Recommendation with Temporal Graph TransformerCode1
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual ActorCode1
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential RecommendationCode1
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential RecommendationCode1
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