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http://dx.doi.org/10.6109/jkiice.2020.24.12.1567

Implementation of User Recommendation System based on Video Contents Story Analysis and Viewing Pattern Analysis  

Lee, Hyoun-Sup (Research Institute of ICT Fusion and Convergence, Dong-Eui University)
Kim, Minyoung (Applied Software Engineering Major, Dong-Eui University)
Lee, Ji-Hoon (Department of software convergence, Dong-Eui University)
Kim, Jin-Deog (Department of Computer Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.12, 2020 , pp. 1567-1573 More about this Journal
Abstract
The development of Internet technology has brought the era of one-man media. An individual produces content on user own and uploads it to related online services, and many users watch the content of online services using devices that allow them to use the Internet. Currently, most users find and watch content they want through search functions provided by existing online services. These features are provided based on information entered by the user who uploaded the content. In an environment where content needs to be retrieved based on these limited word data, user unwanted information is presented to users in the search results. To solve this problem, in this paper, the system actively analyzes the video in the online service, and presents a way to extract and reflect the characteristics held by the video. The research was conducted to extract morphemes based on the story content based on the voice data of a video and analyze them with big data technology.
Keywords
Video analysis; Viewing pattern analysis; Big data; Morpheme;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 S. Y. Yun and S. D. Youn, "A Study on Recommender Technique Applying User Activity and Time Information," The Korea Institute of Information and Communication Engineering, vol. 19, no. 3, pp. 543-551, 2015.   DOI
2 J. H. Lee, H. S. Lee, and J. D. Kim. "Efficient Similarity Extraction Method Between Educational Videos Using the Frequency of Shapes," Journal of the Korean Society for Information and Communication Sciences Conference, vol. 24, no. 1, pp. 9-11, 2020.
3 I. W. Cha, H. S. Lee, and J. D. Kim. "Keyword extraction for measuring similarity between images," Journal of the Korean Society for Information and Communication Sciences Conference, vol. 24, no. 2, pp. 152-154, 2020.
4 Y. S. Woo, H. S. Lee, and J. D. Kim. "Operation of big data SPARK for compare video morphemes and store frequency," Journal of the Korean Society for Information and Communication Sciences Conference, vol. 24, no. 2, pp. 158-160, 2020
5 Y. S. Woo, I. W. Cha, H. S. Lee, and J. D. Kim, "Morphological vector table management for similarity extraction between images," Journal of the Korean Society for Information and Communication Sciences Conference, vol. 24, no. 1, pp. 367-369, 2020
6 K. R. Kim, S. J. Bae, H. S. Lee, and J. D. Kim, "Similarity-based video recommendation system using user and item information," Journal of the Korean Society for Information and Communication Sciences Conference, vol. 24, no. 1, pp. 364-366, 2020.
7 J. H, Lee, H. S. Lee, and J. D. Kim, "A Study on the Use of Frequency of Morphology to Extract Similarities in Lecture Video," Journal of the Korean Society for Information and Communication Sciences Conference, vol. 24, no. 2, pp. 155-157, 2020.
8 J. JiaQi and Y. G. Chung, " A Recommendation Model based on Character-level Deep Convolution Neural Network," The Korea Institute of Information and Communication Engineering, vol. 23, no. 3, pp. 237-246, 2019.
9 H. G. Lee, Y. B. Yun, J. H. Yoon, and G. H. Park, "Context-based Youtube Video Recommendation System Using Video Vector," The Korean Institute of Information Scientists and Engineers, pp. 1495-1497, 2020.