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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 461–470 of 554 papers

TitleStatusHype
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential RecommendationCode0
TriMLP: Revenge of a MLP-like Architecture in Sequential RecommendationCode0
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning AttacksCode0
Prediction then Correction: An Abductive Prediction Correction Method for Sequential RecommendationCode0
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and ItemsCode0
Adaptive User Modeling with Long and Short-Term Preferences for Personalized RecommendationCode0
CSRec: Rethinking Sequential Recommendation from A Causal PerspectiveCode0
MaTrRec: Uniting Mamba and Transformer for Sequential RecommendationCode0
Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain RecommendationCode0
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRecCode0
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