  Content caching at the network edge has been considered an effective way of
mitigating backhaul load and improving user experience. Caching efficiency can
be enhanced by content recommendation and by keeping the information fresh. To
the best of our knowledge, there is no work that jointly takes into account
these aspects. By content recommendation, a requested content that is not in
the cache can be alternatively satisfied by a related cached content
recommended by the system. Information freshness can be quantified by age of
information (AoI). We address, optimal scheduling of cache updates for a
time-slotted system accounting for content recommendation and AoI. For each
content, there are requests that need to be satisfied, and there is a cost
function capturing the freshness of information. We present the following
contributions. First, we prove that the problem is NP-hard. Second, we derive
an integer linear formulation, by which the optimal solution can be obtained
for small-scale scenarios. Third, we develop an algorithm based on Lagrangian
decomposition. Fourth, we develop efficient algorithms for solving the
resulting subproblems. Our algorithm computes a bound that can be used to
evaluate the performance of any suboptimal solution. Finally, we conduct
simulations to show the effectiveness of our algorithm in comparison to a
greedy schedule.
