  The design of effective online caching policies is an increasingly important
problem for content distribution networks, online social networks and edge
computing services, among other areas. This paper proposes a new algorithmic
toolbox for tackling this problem through the lens of optimistic online
learning. We build upon the Follow-the-Regularized-Leader (FTRL) framework
which is developed further here to include predictions for the file requests,
and we design online caching algorithms for bipartite networks with fixed-size
caches or elastic leased caches subject to time-average budget constraints. The
predictions are provided by a content recommendation system that influences the
users viewing activity, and hence can naturally reduce the caching network's
uncertainty about future requests. We prove that the proposed optimistic
learning caching policies can achieve sub-zero performance loss (regret) for
perfect predictions, and maintain the best achievable regret bound $O(\sqrt T)$
even for arbitrary-bad predictions. The performance of the proposed algorithms
is evaluated with detailed trace-driven numerical tests.
