TY - GEN
T1 - Accurate and Visual Video Recommendation Based on Deep Neural Network
AU - Yang, Fan
AU - Li, Gangmin
AU - Yue, Yong
AU - Payne, Terry
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported by the Basic Public Welfare Research Project of Zhejiang (LGF20G020001)， Key Lab of Film and TV Media Technology of Zhejiang Province (No.2020E10015)，and the AI University Research Centre (AI-URC) through the XJTLU Key Program Special Fund (KSF-A-17).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - data visualization
KW - deep neural network
KW - personalized recommendation
KW - video recommendation
UR - http://www.scopus.com/inward/record.url?scp=85136966416&partnerID=8YFLogxK
U2 - 10.1109/ICCCS55155.2022.9846417
DO - 10.1109/ICCCS55155.2022.9846417
M3 - Conference Proceeding
AN - SCOPUS:85136966416
T3 - 2022 7th International Conference on Computer and Communication Systems, ICCCS 2022
SP - 278
EP - 283
BT - 2022 7th International Conference on Computer and Communication Systems, ICCCS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Computer and Communication Systems, ICCCS 2022
Y2 - 22 April 2022 through 25 April 2022
ER -
