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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 8190 of 554 papers

TitleStatusHype
Efficient Failure Pattern Identification of Predictive AlgorithmsCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
Generative Recommender with End-to-End Learnable Item TokenizationCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Exploiting Session Information in BERT-based Session-aware Sequential RecommendationCode1
Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential RecommendationCode1
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-AttentionCode1
Advances in Collaborative Filtering and RankingCode1
Flow Matching based Sequential Recommender ModelCode1
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
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