  Content caching in small base stations or wireless infostations is considered
to be a suitable approach to improve the efficiency in wireless content
delivery. Placing the optimal content into local caches is crucial due to
storage limitations, but it requires knowledge about the content popularity
distribution, which is often not available in advance. Moreover, local content
popularity is subject to fluctuations since mobile users with different
interests connect to the caching entity over time. Which content a user prefers
may depend on the user's context. In this paper, we propose a novel algorithm
for context-aware proactive caching. The algorithm learns context-specific
content popularity online by regularly observing context information of
connected users, updating the cache content and observing cache hits
subsequently. We derive a sublinear regret bound, which characterizes the
learning speed and proves that our algorithm converges to the optimal cache
content placement strategy in terms of maximizing the number of cache hits.
Furthermore, our algorithm supports service differentiation by allowing
operators of caching entities to prioritize customer groups. Our numerical
results confirm that our algorithm outperforms state-of-the-art algorithms in a
real world data set, with an increase in the number of cache hits of at least
14%.
