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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

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Showing 411–420 of 554 papers

TitleStatusHype
Contrastive Enhanced Slide Filter Mixer for Sequential RecommendationCode0
Attacking Pre-trained RecommendationCode0
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
Prediction then Correction: An Abductive Prediction Correction Method for Sequential RecommendationCode0
Conditional Denoising Diffusion for Sequential Recommendation—0
Sequential Recommendation with Probabilistic Logical ReasoningCode0
Learning Graph ODE for Continuous-Time Sequential Recommendation—0
Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation—0
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training—0
Sequential Recommendation with Diffusion Models—0
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