  Contextual bandit problems are a natural fit for many information retrieval
tasks, such as learning to rank, text classification, recommendation, etc.
However, existing learning methods for contextual bandit problems have one of
two drawbacks: they either do not explore the space of all possible document
rankings (i.e., actions) and, thus, may miss the optimal ranking, or they
present suboptimal rankings to a user and, thus, may harm the user experience.
We introduce a new learning method for contextual bandit problems, Safe
Exploration Algorithm (SEA), which overcomes the above drawbacks. SEA starts by
using a baseline (or production) ranking system (i.e., policy), which does not
harm the user experience and, thus, is safe to execute, but has suboptimal
performance and, thus, needs to be improved. Then SEA uses counterfactual
learning to learn a new policy based on the behavior of the baseline policy.
SEA also uses high-confidence off-policy evaluation to estimate the performance
of the newly learned policy. Once the performance of the newly learned policy
is at least as good as the performance of the baseline policy, SEA starts using
the new policy to execute new actions, allowing it to actively explore
favorable regions of the action space. This way, SEA never performs worse than
the baseline policy and, thus, does not harm the user experience, while still
exploring the action space and, thus, being able to find an optimal policy. Our
experiments using text classification and document retrieval confirm the above
by comparing SEA (and a boundless variant called BSEA) to online and offline
learning methods for contextual bandit problems.

Information Retrieval;Machine Learning