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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 311–320 of 554 papers

TitleStatusHype
Multi-level Contrastive Learning Framework for Sequential Recommendation—0
Multimodal Difference Learning for Sequential Recommendation—0
Multimodal Point-of-Interest Recommendation—0
Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning—0
Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model—0
Multi-Tower Multi-Interest Recommendation with User Representation Repel—0
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation—0
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation—0
NxtPost: User to Post Recommendations in Facebook Groups—0
Offline Adaptive Policy Leaning in Real-World Sequential Recommendation Systems—0
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