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Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube

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Published:07 January 2015Publication History
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Abstract

Traditional personalized video recommendation methods focus on utilizing user profile or user history behaviors to model user interests, which follows a static strategy and fails to capture the swift shift of the short-term interests of users. According to our cross-platform data analysis, the information emergence and propagation is faster in social textual stream-based platforms than that in multimedia sharing platforms at micro user level. Inspired by this, we propose a dynamic user modeling strategy to tackle personalized video recommendation issues in the multimedia sharing platform YouTube, by transferring knowledge from the social textual stream-based platform Twitter. In particular, the cross-platform video recommendation strategy is divided into two steps. (1) Real-time hot topic detection: the hot topics that users are currently following are extracted from users' tweets, which are utilized to obtain the related videos in YouTube. (2) Time-aware video recommendation: for the target user in YouTube, the obtained videos are ranked by considering the user profile in YouTube, time factor, and quality factor to generate the final recommendation list. In this way, the short-term (hot topics) and long-term (user profile) interests of users are jointly considered. Carefully designed experiments have demonstrated the advantages 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 11, Issue 2
        December 2014
        197 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/2716635
        Issue’s Table of Contents

        Copyright © 2015 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 January 2015
        • Accepted: 1 May 2014
        • Revised: 1 March 2014
        • Received: 1 October 2013
        Published in tomm Volume 11, Issue 2

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