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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 1–10 of 554 papers

TitleStatusHype
HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User ModelingCode4
Revisiting Self-Attentive Sequential RecommendationCode4
Data Augmentation for Sequential Recommendation: A SurveyCode3
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space ModelsCode2
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential RecommendationCode2
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential RecommendationCode2
Improving Sequential Recommendations with LLMsCode2
Dataset Regeneration for Sequential RecommendationCode2
End-to-end Learnable Clustering for Intent Learning in RecommendationCode2
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential RecommendationCode2
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