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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
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
DiPS: Differentiable Policy for Sketching in Recommender Systems—0
Gumble Softmax For User Behavior Modeling—0
Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation—0
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain RecommendationCode1
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
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