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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
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential RecommendationCode1
Multi-Behavior Sequential Recommendation with Temporal Graph TransformerCode1
Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
Decoupled Side Information Fusion for Sequential RecommendationCode1
GLoSS: Generative Language Models with Semantic Search for Sequential RecommendationCode1
Intent-aware Diffusion with Contrastive Learning for Sequential RecommendationCode1
Debiased Contrastive Learning for Sequential RecommendationCode1
Self-Attentive Sequential RecommendationCode1
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System ExposureCode1
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