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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

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.

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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

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Acknowledgement

Grant : Image/Network-based Intellectual Information Manufacturing Service Research

Supported by : GRRC

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