Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
This addresses the need for more flexible and efficient sequential decision-making in applications like news recommendation and ad placement, though it is incremental as it builds on existing bandit frameworks.
The paper tackles the problem of contextual multi-armed bandit algorithms being overly restrictive by assuming linear reward models, proposing a new algorithm for a semiparametric reward model that supports nonstationarity, which is less restrictive, easier to implement, faster than alternatives, and achieves a tight regret upper bound with the same order as Thompson sampling for linear models.
Contextual multi-armed bandit (MAB) algorithms have been shown promising for maximizing cumulative rewards in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health. However, most of the proposed contextual MAB algorithms assume linear relationships between the reward and the context of the action. This paper proposes a new contextual MAB algorithm for a relaxed, semiparametric reward model that supports nonstationarity. The proposed method is less restrictive, easier to implement and faster than two alternative algorithms that consider the same model, while achieving a tight regret upper bound. We prove that the high-probability upper bound of the regret incurred by the proposed algorithm has the same order as the Thompson sampling algorithm for linear reward models. The proposed and existing algorithms are evaluated via simulation and also applied to Yahoo! news article recommendation log data.