  In addition to maximizing the total revenue, decision-makers in lots of
industries would like to guarantee balanced consumption across different
resources. For instance, in the retailing industry, ensuring a balanced
consumption of resources from different suppliers enhances fairness and helps
main a healthy channel relationship; in the cloud computing industry,
resource-consumption balance helps increase customer satisfaction and reduce
operational costs. Motivated by these practical needs, this paper studies the
price-based network revenue management (NRM) problem with both demand learning
and fair resource-consumption balancing. We introduce the regularized revenue,
i.e., the total revenue with a balancing regularization, as our objective to
incorporate fair resource-consumption balancing into the revenue maximization
goal. We propose a primal-dual-type online policy with the
Upper-Confidence-Bound (UCB) demand learning method to maximize the regularized
revenue. We adopt several innovative techniques to make our algorithm a unified
and computationally efficient framework for the continuous price set and a wide
class of balancing regularizers. Our algorithm achieves a worst-case regret of
$\widetilde O(N^{5/2}\sqrt{T})$, where $N$ denotes the number of products and
$T$ denotes the number of time periods. Numerical experiments in a few NRM
examples demonstrate the effectiveness of our algorithm in simultaneously
achieving revenue maximization and fair resource-consumption balancing
