Skip to search boxSkip to navigationSkip to main content

Accurate and visual video recommendation based on deep neural network

  • Fan Yang
    ,
  • Gangmin Li
    ,
  • Yong Yue
    ,
  • Terry R. Payne
  • Xi'an Jiaotong-Liverpool University
    ,
  • University of Liverpool
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

Video recommendation is vital for a video platform, which provides its users with videos they may be interested in. In this paper, we integrate users' ratings of videos in the video platform and community and crucial information data such as video category, director/actor, predict users' preference for videos through deep neural network, which could improve the accuracy of personalized recommendation. In addition, we use weighted force-directed Graph to show the relationship among users, videos, directors, and other elements, which could display the visualization of data elements and recommended results. Extensive experiments are conducted on three video datasets, and the experimental results demonstrate that the proposed method is more effective than several other recommendation methods.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 278-283 (6 pages)

Publication milestones

  • Published - 15/08/2022

Publication status

Published - 15/08/2022

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

Publication series

  • Publication series name: 2022 7th International Conference on Computer and Communication Systems, ICCCS 2022

ISBN (Print)

9781665450614

ISBN (Electronic)

9781665450607

Publication IDs

  • handle.net: 10547/625921
  • Scopus: 85136966416

Host publication title

2022 7th International Conference on Computer and Communication Systems (ICCCS)

Access to documents

Publication metrics