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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 101110 of 554 papers

TitleStatusHype
Effective and Efficient Training for Sequential Recommendation using Recency SamplingCode1
Efficient Failure Pattern Identification of Predictive AlgorithmsCode1
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative TransformerCode1
Contrastive Cross-Domain Sequential RecommendationCode1
A Systematic Review and Replicability Study of BERT4Rec for Sequential RecommendationCode1
Contrastive Learning for Representation Degeneration Problem in Sequential RecommendationCode1
Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential RecommendationCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Contrastive Learning for Sequential RecommendationCode1
Filter-enhanced MLP is All You Need for Sequential RecommendationCode1
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