Feb 1, 2022 · Our empirical study demonstrates that REN can achieve satisfactory long-term rewards on both synthetic and real-world recommendation datasets.
Modeling and predicting sequential user feedbacks is a core problem in modern e-commerce recommender systems. In this regard, recurrent neural networks (RNN) ...
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Feb 22, 2022 · Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems. However, they usually focus ...
Jun 7, 2024 · Our empirical study demonstrates that REN can achieve satisfactory long-term rewards on both synthetic and real-world recommendation datasets, ...
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Context Uncertainty in Contextual Bandits with Applications to Recommender Systems. H. Wang, Y. Ma, H. Ding, and Y. Wang. AAAI, page 8539-8547. AAAI Press ...
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems. AAAI 2022 Oral. Hao Wang, Yifei Ma, Hao Ding, Yuyang (Bernie) Wang.
Jun 4, 2024 · This paper focuses on a joint neural contextual bandit solution which serves all recommending items in one single model.
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By acknowledging the uncertainty in the data and deliberately exploring to reduce it, bandits learn about the relevance of unexplored items. This is especially ...