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Boosting Video Popularity through Recommendation Systems

By Renjie Zhou, Samamon Khemmarat, Lixin Gao and Huiqiang Wang

Abstract

While search engines are the major sources of content discovery on online content providers and e-commerce sites, their capability is limited since textual descriptions cannot fully describe the semantic of content such as videos. Recommendation systems are now widely used in online content providers and e-commerce sites and play an important role in discovering content. In this paper, we describe how one can boost the popularity of a video through the recommendation system in YouTube. We present a model that captures the view propagation between videos through the recommendation linkage and quantifies the influence that a video has on the popularity of another video. Furthermore, we identify that the similarity in titles and tags is an important factor in forming the recommendation linkage between videos. This suggests that one can manipulate the metadata of a video to boost its popularity

Topics: Categories and Subject Descriptors H.4 [Information Systems, Applications General Terms Algorithms, Measurement, Human Factors Keywords YouTube, Recommendation System, View Propagation, Video Popularity
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.4986
Provided by: CiteSeerX
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