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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 481–490 of 554 papers

TitleStatusHype
Gumble Softmax For User Behavior Modeling—0
Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation—0
Route Optimization via Environment-Aware Deep Network and Reinforcement Learning—0
TEA: A Sequential Recommendation Framework via Temporally Evolving AggregationsCode0
Supervised Advantage Actor-Critic for Recommender Systems—0
Sequential Movie Genre Prediction using Average Transition Probability with Clustering—0
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning—0
Learning to Learn a Cold-start Sequential Recommender—0
Self-supervised Learning for Sequential Recommendation with Model Augmentation—0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
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