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) ...
People also ask
What is the uncertainty of a recommendation system?
What is context in contextual bandit?
What is contextual bandits for recommendation?
What is context in recommender system?
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, ...
Duration: 23:40
Posted: Feb 3, 2022
Posted: Feb 3, 2022
Dive into the research topics of 'Context Uncertainty in Contextual Bandits with Applications to Recommender Systems'. Together they form a unique fingerprint.
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.
Missing: Applications | Show results with:Applications
By acknowledging the uncertainty in the data and deliberately exploring to reduce it, bandits learn about the relevance of unexplored items. This is especially ...