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LGA: latent genre aware micro-video recommendation on social media

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Abstract

Social media has evolved into one of the most important channels to share micro-videos nowadays. The sheer volume of micro-videos available in social networks often undermines users’ capability to choose the micro-videos that best fit their interests. Recommendation appear as a natural solution to this problem. However, existing video recommendation methods only consider the users’ historical preferences on videos, without exploring any video contents. In this paper, we develop a novel latent genre aware micro-video recommendation model to solve the problem. First, we extract user-item interaction features, and auxiliary features describing both contextual and visual contents of micro-videos. Second, these features are fed into the neural recommendation model that simultaneously learns the latent genres of micro-videos and the optimal recommendation scores. Experiments on real-world dataset demonstrate the effectiveness and the efficiency of our proposed method compared with several state-of-the-art approaches.

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Notes

  1. Vine: https://vine.co

  2. Vine report: http://blog.vine.co

  3. https://www.youtube.com/?hl=zh-cn

  4. https://www.msn.com/en-us/video

  5. https://video.search.yahoo.com/

  6. Twitter Streaming APIs: https://dev.twitter.com/streaming/overview

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Authors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Jingwei Ma, Mingyang Zhong, Xin Zhao, Lei Zhu & Xue Li

  2. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Guang Li

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  1. Jingwei Ma
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  2. Guang Li
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  3. Mingyang Zhong
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  4. Xin Zhao
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  5. Lei Zhu
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  6. Xue Li
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Correspondence to Mingyang Zhong.

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Ma, J., Li, G., Zhong, M. et al. LGA: latent genre aware micro-video recommendation on social media. Multimed Tools Appl 77, 2991–3008 (2018). https://doi.org/10.1007/s11042-017-4827-2

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  • DOI: https://doi.org/10.1007/s11042-017-4827-2

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