  With the growth of user-generated content, we observe the constant rise of
the number of companies, such as search engines, content aggregators, etc.,
that operate with tremendous amounts of web content not being the services
hosting it. Thus, aiming to locate the most important content and promote it to
the users, they face the need of estimating the current and predicting the
future content popularity.
  In this paper, we approach the problem of video popularity prediction not
from the side of a video hosting service, as done in all previous studies, but
from the side of an operating company, which provides a popular video search
service that aggregates content from different video hosting websites. We
investigate video popularity prediction based on features from three primary
sources available for a typical operating company: first, the content hosting
provider may deliver its data via its API, second, the operating company makes
use of its own search and browsing logs, third, the company crawls information
about embeds of a video and links to a video page from publicly available
resources on the Web. We show that video popularity prediction based on the
embed and link data coupled with the internal search and browsing data
significantly improves video popularity prediction based only on the data
provided by the video hosting and can even adequately replace the API data in
the cases when it is partly or completely unavailable.
