Erik Altman, Jovan Blanusa, et al.
NeurIPS 2023
In various recommender system applications, from medical diagnosis to dialog systems, due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration; however, the agent has a freedom to choose which variables to observe. In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach, adapting it to Context-Attentive Bandit setting. We provide a theoretical regret analysis and an extensive empirical evaluation demonstrating advantages of the proposed approach over several baseline methods on a variety of real-life datasets.
Erik Altman, Jovan Blanusa, et al.
NeurIPS 2023
Daniel Fišer, Daniel Gnad, et al.
IJCAI 2021
Haoran Zhu, Pavankumar Murali, et al.
NeurIPS 2020
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024