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Personalized Video Recommendation through Graph Propagation

Published:04 July 2014Publication History
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Abstract

The rapid growth of the number of videos on the Internet provides enormous potential for users to find content of interest. However, the vast quantity of videos also turns the finding process into a difficult task. In this article, we address the problem of providing personalized video recommendation for users. Rather than only exploring the user-video bipartite graph that is formulated using click information, we first combine the clicks and queries information to build a tripartite graph. In the tripartite graph, the query nodes act as bridges to connect user nodes and video nodes. Then, to further enrich the connections between users and videos, three subgraphs between the same kinds of nodes are added to the tripartite graph by exploring content-based information (video tags and textual queries). We propose an iterative propagation algorithm over the enhanced graph to compute the preference information of each user. Experiments conducted on a dataset with 1,369 users, 8,765 queries, and 17,712 videos collected from a commercial video search engine demonstrate the effectiveness of the proposed method.

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            • Published in

              cover image ACM Transactions on Multimedia Computing, Communications, and Applications
              ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 10, Issue 4
              June 2014
              132 pages
              ISSN:1551-6857
              EISSN:1551-6865
              DOI:10.1145/2656131
              Issue’s Table of Contents

              Copyright © 2014 ACM

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 4 July 2014
              • Revised: 1 February 2014
              • Accepted: 1 February 2014
              • Received: 1 June 2013
              Published in tomm Volume 10, Issue 4

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