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Found 2 papers, 1 papers with code
MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems
The proposed method takes advantage of the characteristics of recommender systems and draws ideas from the model-based reinforcement learning method for higher sample efficiency.
Sequential Evaluation and Generation Framework for Combinatorial Recommender System
On the one hand of this framework, an evaluation model is trained to evaluate the expected overall utility, by fully considering the user, item information and the correlations among the co-exposed items.
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