  We consider a new setting of online clustering of contextual cascading
bandits, an online learning problem where the underlying cluster structure over
users is unknown and needs to be learned from a random prefix feedback. More
precisely, a learning agent recommends an ordered list of items to a user, who
checks the list and stops at the first satisfactory item, if any. We propose an
algorithm of CLUB-cascade for this setting and prove a $T$-step regret bound of
order $\tilde{O}(\sqrt{T})$. Previous work corresponds to the degenerate case
of only one cluster, and our general regret bound in this special case also
significantly improves theirs. We conduct experiments on both synthetic and
real data, and demonstrate the effectiveness of our algorithm and the advantage
of incorporating online clustering method.
