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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 121130 of 554 papers

TitleStatusHype
Debiased Contrastive Learning for Sequential RecommendationCode1
EAGER: Two-Stream Generative Recommender with Behavior-Semantic CollaborationCode1
Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
Effective and Efficient Training for Sequential Recommendation using Recency SamplingCode1
Online Distillation-enhanced Multi-modal Transformer for Sequential RecommendationCode1
Dually Enhanced Propensity Score Estimation in Sequential RecommendationCode1
Self-Attentive Sequential RecommendationCode1
Dynamic Graph Neural Networks for Sequential RecommendationCode1
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System ExposureCode1
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