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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 261–270 of 554 papers

TitleStatusHype
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Learning Robust Sequential Recommenders through Confident Soft LabelsCode0
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential RecommendationCode0
AutoSAM: Towards Automatic Sampling of User Behaviors for Sequential Recommender SystemsCode0
Generate What You Prefer: Reshaping Sequential Recommendation via Guided DiffusionCode1
Large Language Model Can Interpret Latent Space of Sequential RecommenderCode1
Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model—0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems—0
Intent Contrastive Learning with Cross Subsequences for Sequential RecommendationCode1
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