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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 531–540 of 554 papers

TitleStatusHype
AdaptRec: A Self-Adaptive Framework for Sequential Recommendations with Large Language Models—0
LLM4DSR: Leveraing Large Language Model for Denoising Sequential Recommendation—0
LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation—0
LLM-based User Profile Management for Recommender System—0
LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation—0
Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation—0
LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation—0
Local Policy Improvement for Recommender Systems—0
Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation—0
Look into the Future: Deep Contextualized Sequential Recommendation—0
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