Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Multimodal Media Content Classification using Keyword Weighting for Recommendation

추천을 위한 키워드 가중치를 이용한 멀티모달 미디어 콘텐츠 분류

  • Kang, Ji-Soo (Department of Computer Science, Kyonggi University) ;
  • Baek, Ji-Won (Department of Computer Science, Kyonggi University) ;
  • Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
  • 강지수 (경기대학교 컴퓨터과학과) ;
  • 백지원 (경기대학교 컴퓨터과학과) ;
  • 정경용 (경기대학교 컴퓨터공학부)
  • Received : 2019.02.22
  • Accepted : 2019.05.20
  • Published : 2019.05.28
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Abstract

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.

모바일 시장의 확장과 함께 멀티모달 미디어 콘텐츠의 제공을 위한 플랫폼이 다양해지고 있다. 멀티모달 미디어 콘텐츠에는 이종데이터들이 복합적으로 포함되어 있어 사용자들이 선호 콘텐츠를 선택하기 위해 시간과 노력이 요구된다. 따라서 본 논문에서는 추천을 위한 키워드 가중치를 이용한 멀티모달 미디어 콘텐츠 분류를 제안한다. 제안하는 방법은 멀티모달 미디어 콘텐츠의 텍스트 데이터에서 키워드 가중치를 통해 콘텐츠를 가장 잘 나타내는 키워드를 추출한다. 추출된 키워드를 기반으로 서브클래스를 갖는 장르 클래스를 생성하고 이에 적절한 멀티모달 미디어 콘텐츠를 분류한다. 또한 개인화된 추천을 위해 사용자의 선호도 평가를 진행하여 사용자의 콘텐츠 선호도 분석 결과를 기반으로 멀티모달 콘텐츠를 추천한다. 성능평가는 추천 결과의 정확도와 만족도를 통해 우수함을 검증한다. 이는 사용자가 선호하는 장르와 키워드를 모두 고려하여 추천하기 때문에 정확도는 74.62%, 만족도는 69.1%로 높게 나타난다.

Keywords

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Fig. 1. Sub-classes included {Thriller, Action} Class

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Fig. 2. Process of Multimodal Media Content Classification using Keyword Weighting for Recommendation

Table 1. Movie Keyword Extracted through Text Mining

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Table 2. Performance Results of Recommendation Method

JKOHBZ_2019_v9n5_1_t0002.png 이미지

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