Browse > Article
http://dx.doi.org/10.6109/jkiice.2021.25.12.1716

Big Data Analysis Method for Recommendations of Educational Video Contents  

Lee, Hyoun-Sup (Department of Applied SW Engineering, Dong-Eui University)
Kim, JinDeog (Department of Computer Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.12, 2021 , pp. 1716-1722 More about this Journal
Abstract
Recently, the capacity of video content delivery services has been increasing significantly. Therefore, the importance of user recommendation is increasing. In addition, these contents contain a variety of characteristics, making it difficult to express the characteristics of the content properly only with a few keywords(Elements used in the search, such as titles, tags, topics, words, etc.) specified by the user. Consequently, existing recommendation systems that use user-defined keywords have limitations that do not properly reflect the characteristics of objects. In this paper, we compare the efficiency of between a method using voice data-based subtitles and an image comparison method using keyframes of images in recommendation module of educational video service systems. Furthermore, we propose the types and environments of video content in which each analysis technique can be efficiently utilized through experimental results.
Keywords
User recommendation; Voice and subtitle analysis; Keyframe analysis; Bigdata;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Video streaming service site [Internet]. Available: http://www.youtube.com.
2 video streaming entertainment company [Internet]. Available: http://www.netflex.com.
3 Google AutoML Vision [Internet]. Available: https://cloud.google.com/automl/?hl=ko#.
4 Google Vision API [Internet]. Available: https://cloud.google.com/vision/?hl=ko#.
5 W. S. Ha, "Collaborative Filtering using Web Documents Classification by Associative Word Frequency," Inha University Master's Thesis, 2005.
6 K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," CVPR 2016, pp. 770-778, 2016.
7 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.
8 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.
9 J. E. Son, S. B. Kim, H. J. Kim, and S. Z. Cho, "Review and Analysis of Recommender Systems," Journal of the Korean Institute of Industrial Engineers, vol. 41, no. 2, pp. 185-208, Apr. 2015.   DOI
10 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.
11 H. S. Lee and J. D. Kim, "A Design of Similar Video Recommendation System using Extracted Words in Big Data Cluster," Journal of Korea Institute of Information and Communication Engineering, vol. 24, no. 2, pp. 172-178, 2020.   DOI
12 UCF-101 is a created human behavior recognition model and consists of 101 classes [Internet]. Available: https://www.crcv.ucf.edu/data/UCF101.php.