The Short Video Popularity Prediction Using Internet of Things and Deep Learning

doi:10.1109/ACCESS.2024.3383060

CQUET-IR

	The Short Video Popularity Prediction Using Internet of Things and Deep Learning
	He, Zichen 1; Li, Danian 2
	2024
发表期刊	IEEE ACCESS
ISSN	2169-3536
EISSN	2169-3536
卷号	12 页码:47508-47517
摘要	In order to furnish valuable insights and solutions applicable to content creators, social media platforms, academic research, and general users, this investigation integrates the Internet of Things (IoT) with deep learning regression models to examine methodologies for predicting the popularity of short videos. Within the context of cross-cultural communication, a proposed Content Popularity Rank Prediction based on the Convolutional Neural Network (CPRP-CNN) model relies exclusively on the personal attributes of the publisher and the textual characteristics of short videos to anticipate the viewership levels of short videos promptly following their release. Through simulated experiments, the model's performance is assessed, revealing that the utilization of the Rectified Linear Unit (Relu) activation function in the CPRP-CNN model enhances accuracy by 42.2% when contrasted with the use of the sigmoid function. This enhancement is coupled with a 37.8% reduction in cross-entropy loss. Furthermore, the proposed CPRP-CNN model attains a cross-entropy of 0.692 and an accuracy of 74.7%, exhibiting superior Mean Squared Error (MSE) and Mean Absolute Error (MAE) values of 2.728 and 1.751, respectively, when compared to alternative prediction models. These outcomes signify that the amalgamation of deep learning models with fused features within the IoT context significantly ameliorates the predictive efficacy of short video popularity. The research findings contribute to the enhancement of personalized and precise short video content recommendations.
关键词	Cross-cultural communication deep learning regression model short video popularity prediction Internet of Things
DOI	10.1109/ACCESS.2024.3383060
收录类别	SCIE ; EI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001197741500001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号	20241415844669
原始文献类型	Article
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/18360
专题	重庆电子科技职业大学
作者单位	1.Chongqing Normal Univ, Sch Journalism & Commun, Chongqing 401331, Peoples R China; 2.Chongqing Coll Elect Engn, Sch Elect & Internet Things, Chongqing 401331, Peoples R China
推荐引用方式 GB/T 7714	He, Zichen,Li, Danian. The Short Video Popularity Prediction Using Internet of Things and Deep Learning[J]. IEEE ACCESS,2024,12:47508-47517.
APA	He, Zichen,&Li, Danian.(2024).The Short Video Popularity Prediction Using Internet of Things and Deep Learning.IEEE ACCESS,12,47508-47517.
MLA	He, Zichen,et al."The Short Video Popularity Prediction Using Internet of Things and Deep Learning".IEEE ACCESS 12(2024):47508-47517.