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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 241–250 of 554 papers

TitleStatusHype
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation—0
CSRN: Collaborative Sequential Recommendation Networks for News Retrieval—0
ID-Agnostic User Behavior Pre-training for Sequential Recommendation—0
ID-centric Pre-training for Recommendation—0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
A Survey on Reinforcement Learning for Recommender Systems—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
Conditional Denoising Diffusion for Sequential Recommendation—0
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