  We study content caching with recommendations in a wireless network where the
users are connected through a base station equipped with a finite-capacity
cache. We assume a fixed set of contents with unknown user preferences and
content popularities. We can recommend a subset of the contents to the users
which encourages the users to request these contents. Recommendation can thus
be used to increase cache hits. We formulate the cache hit optimization problem
as a combinatorial multi-armed bandit (CMAB). We propose a UCB-based algorithm
to decide which contents to cache and recommend. We provide an upper bound on
the regret of our algorithm. We numerically demonstrate the performance of our
algorithm and compare it to state-of-the-art algorithms.
