Context Attentive Bandits: Contextual Bandit with Restricted Context

Context Attentive Bandits: Contextual Bandit with Restricted Context

Djallel Bouneffouf, Irina Rish, Guillermo Cecchi, Raphaël Féraud

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 1468-1475. https://doi.org/10.24963/ijcai.2017/203

We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommender systems and attention modeling.Herein, we adapt the standard multi-armed bandit algorithm known as Thompson Sampling to take advantage of our restricted context setting, and propose two novel algorithms, called the Thompson Sampling with Restricted Context (TSRC) and the Windows Thompson Sampling with Restricted Context (WTSRC), for handling stationary and nonstationary environments, respectively. Our empirical results demonstrate advantages of the proposed approaches on several real-life datasets.
Keywords:
Machine Learning: Online Learning
Machine Learning: Reinforcement Learning
Machine Learning: Machine Learning
Machine Learning: New Problems