Context Attentive Bandits: Contextual Bandit with Restricted Context
This addresses online decision-making problems in domains like clinical trials and recommender systems, but it appears incremental as it adapts an existing method to a new setting.
The paper tackles the problem of contextual bandits with restricted context, where only a limited number of features are accessible per iteration, by proposing two novel algorithms based on Thompson Sampling for stationary and nonstationary environments. The result shows advantages of the proposed approaches on several real-life datasets, though no concrete numbers are provided.
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