Multimedia Tools and Applications

, Volume 77, Issue 3, pp 2991–3008 | Cite as

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

  • Jingwei Ma
  • Guang Li
  • Mingyang Zhong
  • Xin Zhao
  • Lei Zhu
  • Xue Li
Article
  • 136 Downloads

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.

Keywords

Micro-video recommendation Genre aware Neural network 

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Jingwei Ma
    • 1
  • Guang Li
    • 2
  • Mingyang Zhong
    • 1
  • Xin Zhao
    • 1
  • Lei Zhu
    • 1
  • Xue Li
    • 1
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
  2. 2.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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