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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 331340 of 554 papers

TitleStatusHype
Prediction then Correction: An Abductive Prediction Correction Method for Sequential RecommendationCode0
Self-Supervised Multi-Modal Sequential RecommendationCode1
Sequential Recommendation with Probabilistic Logical ReasoningCode0
Conditional Denoising Diffusion for Sequential Recommendation0
Frequency Enhanced Hybrid Attention Network for Sequential RecommendationCode1
Attention Mixtures for Time-Aware Sequential RecommendationCode1
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential RecommendationCode1
Meta-optimized Contrastive Learning for Sequential RecommendationCode1
Learning Graph ODE for Continuous-Time Sequential Recommendation0
Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation0
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