Quality biased multimedia data retrieval in microblogs |
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| Affiliation: | 1. Computer Application Research Center, ShenZhen Graduate School, Harbin Institute of Technology, ShenZhen, China;2. School of Electronic Information Engineering, Tianjin University, Tianjin, China;3. School of Computing, National University of Singapore, Singapore;1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Information, Qilu University of Technology, Jinan 250353, China;1. School of information and technology, Hebei University of Economics and Business, China;2. School of Computer Science, University of Adelaide, Australia;3. School of Computer and Information Technology, Beijing Jiaotong University, China |
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| Abstract: | With the rapid development of social media platforms, huge amount of user generated contents (UGC) are generated ceaselessly. In recent years, content based microblog retrieval has attracted extensive research attention. Effective microblog retrieval services complex analysis of short text and multimedia contents. In this paper, we present a quality biased multimedia microblog retrieval framework. First, we develop an anchor graph based multiview embedding framework which maps the multimedia content features into a unified latent space. Then, the content matching scores of testing microblogs related to the query are obtained by a Markov random field. Further, we employ an quality model to incorporate both microblog quality and content matching. As compared with the state-of-art methods, experimental results demonstrate the effectiveness of the proposed approach. |
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| Keywords: | Microblog retrieval Quality model Multiview embedding 00-01 99-00 |
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