Web multimedia content has reached much importance
lately. One of the most important content types is online
video, as demonstrated by the success of platforms such as
YouTube. The growth in the volume of available online video
is also observed in corporate scenarios, such as TV station.
This paper evaluates a set of corporate online videos hosted
by Sambatech, a company that holds the largest platform for
online multimedia content distribution in Latin America. We
propose a novel analytical approach for video recommendation,
focusing on video objects being consumed. After modeling this
service, we characterize the contents from multiple sources, and
propose techniques for multimedia content recommendation.
Experimental results indicate that the proposed method is very
promising, which had obtained almost 70% in precision. We
also perform distinct evaluations using different approaches
from literature, such as the state-of-the-art technique for item
recommendation