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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

Showing 381390 of 554 papers

TitleStatusHype
Multi-level Contrastive Learning Framework for Sequential Recommendation0
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation0
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed VideosCode1
Improving Micro-video Recommendation by Controlling Position Bias0
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation0
Time Lag Aware Sequential Recommendation0
UFNRec: Utilizing False Negative Samples for Sequential RecommendationCode0
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Sparse Attentive Memory Network for Click-through Rate Prediction with Long SequencesCode0
Generating Negative Samples for Sequential Recommendation0
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