Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
Analysis of social voting patterns on digg
Proceedings of the first workshop on Online social networks
Using a model of social dynamics to predict popularity of news
Proceedings of the 19th international conference on World wide web
Predicting the popularity of online content
Communications of the ACM
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
The impact of YouTube recommendation system on video views
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
What should you cache?: a global analysis on YouTube related video caching
Proceeding of the 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video
Cache-centric video recommendation: an approach to improve the efficiency of YouTube caches
Proceedings of the 4th ACM Multimedia Systems Conference
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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.