  Motivated by problems of learning to rank long item sequences, we introduce a
variant of the cascading bandit model that considers flexible length sequences
with varying rewards and losses. We formulate two generative models for this
problem within the generalized linear setting, and design and analyze upper
confidence algorithms for it. Our analysis delivers tight regret bounds which,
when specialized to vanilla cascading bandits, results in sharper guarantees
than previously available in the literature. We evaluate our algorithms on a
number of real-world datasets, and show significantly improved empirical
performance as compared to known cascading bandit baselines.
