  Cascading bandit (CB) is a popular model for web search and online
advertising, where an agent aims to learn the $K$ most attractive items out of
a ground set of size $L$ during the interaction with a user. However, the
stationary CB model may be too simple to apply to real-world problems, where
user preferences may change over time. Considering piecewise-stationary
environments, two efficient algorithms, \texttt{GLRT-CascadeUCB} and
\texttt{GLRT-CascadeKL-UCB}, are developed and shown to ensure regret upper
bounds on the order of $\mathcal{O}(\sqrt{NLT\log{T}})$, where $N$ is the
number of piecewise-stationary segments, and $T$ is the number of time slots.
At the crux of the proposed algorithms is an almost parameter-free change-point
detector, the generalized likelihood ratio test (GLRT). Comparing with existing
works, the GLRT-based algorithms: i) are free of change-point-dependent
information for choosing parameters; ii) have fewer tuning parameters; iii)
improve at least the $L$ dependence in regret upper bounds. In addition, we
show that the proposed algorithms are optimal (up to a logarithm factor) in
terms of regret by deriving a minimax lower bound on the order of
$\Omega(\sqrt{NLT})$ for piecewise-stationary CB. The efficiency of the
proposed algorithms relative to state-of-the-art approaches is validated
through numerical experiments on both synthetic and real-world datasets.
