Contextual Bandit with Missing Rewards
This addresses a practical issue in online applications like clinical trials and ad recommendations where rewards are not always observed, though it appears incremental.
The paper tackles the contextual bandit problem with missing rewards by combining standard contextual bandit methods with unsupervised clustering to estimate missing rewards, enabling learning from all events, and reports promising empirical results on real-life datasets.
We consider a novel variant of the contextual bandit problem (i.e., the multi-armed bandit with side-information, or context, available to a decision-maker) where the reward associated with each context-based decision may not always be observed("missing rewards"). This new problem is motivated by certain online settings including clinical trial and ad recommendation applications. In order to address the missing rewards setting, we propose to combine the standard contextual bandit approach with an unsupervised learning mechanism such as clustering. Unlike standard contextual bandit methods, by leveraging clustering to estimate missing reward, we are able to learn from each incoming event, even those with missing rewards. Promising empirical results are obtained on several real-life datasets.