Online Clustering of Contextual Cascading Bandits

Understand

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.