Modeling, characterizing and recommendation in multimedia web content services.

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.