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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 376–400 of 554 papers

TitleStatusHype
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
Learning Post-Hoc Causal Explanations for Recommendation—0
Gumble Softmax For User Behavior Modeling—0
Learning to Augment for Casual User Recommendation—0
Learning to Learn a Cold-start Sequential Recommender—0
Learning to Structure Long-term Dependence for Sequential Recommendation—0
Unifying Generative and Dense Retrieval for Sequential Recommendation—0
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation—0
Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision—0
Lightweight Self-Attentive Sequential Recommendation—0
X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation—0
Sequential recommendation with metric models based on frequent sequences—0
Sequential Recommendation with Relation-Aware Kernelized Self-Attention—0
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
Sequential Recommendation with User Evolving Preference Decomposition—0
M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation—0
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