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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 141–150 of 554 papers

TitleStatusHype
SIGMA: Selective Gated Mamba for Sequential RecommendationCode1
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation—0
Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items—0
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
Harnessing Multimodal Large Language Models for Multimodal Sequential RecommendationCode1
ELASTIC: Efficient Linear Attention for Sequential Interest Compression—0
SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation—0
Modeling Domain and Feedback Transitions for Cross-Domain Sequential Recommendation—0
LLM4DSR: Leveraing Large Language Model for Denoising Sequential Recommendation—0
An Efficient Continuous Control Perspective for Reinforcement-Learning-based Sequential Recommendation—0
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