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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 531540 of 554 papers

TitleStatusHype
Neighborhood-based Hard Negative Mining for Sequential RecommendationCode0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
Contrastive Enhanced Slide Filter Mixer for Sequential RecommendationCode0
Behavior-Dependent Linear Recurrent Units for Efficient Sequential RecommendationCode0
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language ModelingCode0
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from TransformerCode0
One Person, One Model--Learning Compound Router for Sequential RecommendationCode0
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential RecommendationCode0
Disentangling Past-Future Modeling in Sequential Recommendation via Dual NetworksCode0
Learning Positional Attention for Sequential RecommendationCode0
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