  In this paper, we propose and study opportunistic contextual bandits - a
special case of contextual bandits where the exploration cost varies under
different environmental conditions, such as network load or return variation in
recommendations. When the exploration cost is low, so is the actual regret of
pulling a sub-optimal arm (e.g., trying a suboptimal recommendation).
Therefore, intuitively, we could explore more when the exploration cost is
relatively low and exploit more when the exploration cost is relatively high.
Inspired by this intuition, for opportunistic contextual bandits with Linear
payoffs, we propose an Adaptive Upper-Confidence-Bound algorithm (AdaLinUCB) to
adaptively balance the exploration-exploitation trade-off for opportunistic
learning. We prove that AdaLinUCB achieves O((log T)^2) problem-dependent
regret upper bound, which has a smaller coefficient than that of the
traditional LinUCB algorithm. Moreover, based on both synthetic and real-world
dataset, we show that AdaLinUCB significantly outperforms other contextual
bandit algorithms, under large exploration cost fluctuations.
