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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 441–450 of 554 papers

TitleStatusHype
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial ActionsCode0
Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation—0
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential RecommendationCode0
Multi-level Contrastive Learning Framework for Sequential Recommendation—0
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation—0
Improving Micro-video Recommendation by Controlling Position Bias—0
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation—0
Time Lag Aware Sequential Recommendation—0
UFNRec: Utilizing False Negative Samples for Sequential RecommendationCode0
Sparse Attentive Memory Network for Click-through Rate Prediction with Long SequencesCode0
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