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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 431–440 of 554 papers

TitleStatusHype
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models—0
Filter-enhanced MLP is All You Need for Sequential RecommendationCode1
NxtPost: User to Post Recommendations in Facebook Groups—0
Intent Contrastive Learning for Sequential RecommendationCode1
Sequential Search with Off-Policy Reinforcement Learning—0
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and ItemsCode0
Sequential Recommendation via Stochastic Self-AttentionCode1
CSSR: A Context-Aware Sequential Software Service Recommendation ModelCode0
GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation—0
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-trainingCode0
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