Proceedings

Toward Optimal Solution for the Context-Attentive Bandit Problem

Djallel Bouneffouf, Raphael Feraud, Sohini Upadhyay, Irina Rish, Yasaman Khazaeni

Download BibTeX Cite

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3493-3500.

https://doi.org/10.24963/ijcai.2021/481

Keywords

  • Data Mining: Recommender Systems
  • Machine Learning Applications: Applications of Reinforcement Learning
  • Data Mining: Big Data, Large-Scale Systems

Abstract

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.

Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Irina Rish, and Yasaman Khazaeni. 2021. Toward Optimal Solution for the Context-Attentive Bandit Problem. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3493–3500. https://doi.org/10.24963/ijcai.2021/481
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards