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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 231–240 of 554 papers

TitleStatusHype
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation—0
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai—0
Farzi Data: Autoregressive Data Distillation—0
Context-aware Sequential Recommendation—0
HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
A Survey on Sequential Recommendation—0
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings—0
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