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 difficulty 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 efficiently 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. Categories and Subject Descriptors H.3.3 [Information Systems]: Information Storage and Retrieval--Information Search and Retrieval General Terms Algorithms Keywords Recommendation systems, label propagation, collaborative filtering, random walks, video search

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Shumeet Baluja, Rohan Seth, D. Sivakumar, Yushi Jing, Jay Yagnik, Shankar Kumar, Deepak Ravichandran, Mohamed Aly
• Keywords: Algorithms Keywords Recommendation, Entire User Video Graph, Internet Technology, Personalized Video Suggestions, WWW 2008