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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 381–390 of 554 papers

TitleStatusHype
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
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