Video Suggestion and Discovery for YouTube: Taking Random Walks Through the View Graph
The rapid growth of the number of videos in YouTube provides enormous potential for users to find content of interest to them. Unfortunately, given the diculty of searching videos, the size of the video repository also makes the discovery of new content a daunting task. In this paper, we present a novel method based upon the analysis of the entire user–video graph to provide personalized video suggestions for users. The resulting algorithm, termed Adsorption, provides a simple method to effciently propagate preference information through a variety of graphs. We extensively test the results of the recommendations on a three month snapshot of live data from YouTube.
Recommendation systems label propagation collaborative filtering random walks video search
Shumeet Baluja Rohan Seth D. Sivakumar Yushi Jing Jay Yagnik Shankar Kumar Deepak Ravichandran Mohamed Aly
Google, Inc.Mountain View, CA, USA
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）