Accurate and Visual Video Recommendation Based on Deep Neural Network

Yang, Fan, Li, Gangmin, Yue, Yong and Payne, Terry ORCID: 0000-0002-0106-8731 (2022) Accurate and Visual Video Recommendation Based on Deep Neural Network. In: 2022 7th International Conference on Computer and Communication Systems (ICCCS), 2022-4-22 - 2022-4-25.

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Official URL: http://dx.doi.org/10.1109/icccs55155.2022.9846417

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.

Item Type:	Conference or Workshop Item (Unspecified)
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	16 Nov 2023 08:25
Last Modified:	15 Mar 2024 12:51
DOI:	10.1109/icccs55155.2022.9846417
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3176823