Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Big Data Analysis Method for Recommendations of Educational Video Contents

사용자 추천을 위한 교육용 동영상의 빅데이터 분석 기법 비교

  • Lee, Hyoun-Sup (Department of Applied SW Engineering, Dong-Eui University) ;
  • Kim, JinDeog (Department of Computer Engineering, Dong-Eui University)
  • Received : 2021.10.14
  • Accepted : 2021.11.24
  • Published : 2021.12.31
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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.

최근 동영상 콘텐츠 제공 서비스는 그 용량이 매우 증가하여 사용자 추천의 중요성이 증가하고 있다. 그리고 이러한 콘텐츠는 다양한 특성을 내포하고 있어 사용자가 지정한 키워드만으로 그 콘텐츠의 특징을 제대로 표현하기 어렵다. 그러므로 사용자가 정의한 키워드를 이용하는 기존의 추천 시스템은 개체의 특성을 제대로 반영하지 못하는 한계가 있다. 본 논문에서는 교육용 동영상 서비스 시스템의 콘텐츠 추천을 위한 기법 중 음성데이터 기반 자막을 이용한 분석과 영상의 키프레임을 이용한 영상 비교 기법의 효율성을 비교한다. 또한, 실험 결과를 통해 각 분석 기법이 효율적으로 활용될 수 있는 영상 콘텐츠의 유형 및 환경을 제안한다.

Keywords

Acknowledgement

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2021-2020-0-01791) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)

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