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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
Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential RecommendationCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential RecommendationCode1
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential RecommendationCode1
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
An Empirical Study of Training ID-Agnostic Multi-modal Sequential RecommendersCode1
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-AttentionCode1
Advances in Collaborative Filtering and RankingCode1
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional TransformersCode1
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative TransformerCode1
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