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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 231–240 of 554 papers

TitleStatusHype
Rethinking Large Language Model Architectures for Sequential Recommendations—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Improving Sequential Recommendations with LLMsCode2
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning AttacksCode0
Privacy-Preserving Cross-Domain Sequential RecommendationCode0
Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision—0
End-to-end Learnable Clustering for Intent Learning in RecommendationCode2
A Survey on Cross-Domain Sequential RecommendationCode1
Prompt-based Multi-interest Learning Method for Sequential RecommendationCode0
Privacy-Preserving Sequential Recommendation with Collaborative Confusion—0
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