Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Measurement and analysis of a streaming-media workload
USITS'01 Proceedings of the 3rd conference on USENIX Symposium on Internet Technologies and Systems - Volume 3
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
Recommender Systems Handbook
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Web content has gained 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 networks. 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, and not on consumer profile data. After modeling this service, we characterize the contents from multiple sources, and propose techniques for video recommendation. Experimental results indicate that the proposed method obtains a gain of about 42% in precision for a set of five recommendations, as compared to a baseline that is based only on video metadata.