Developing the online reviews based recommender models for multi-attributes using deep learning

딥러닝을 이용한 온라인 리뷰 기반 다속성별 추천 모형 개발

  • Received : 2019.01.15
  • Accepted : 2019.03.29
  • Published : 2019.03.31


Purpose The purpose of this study is to deduct the factors for explaining the economic behavior of an Internet user who provides personal information notwithstanding the concern about an invasion of privacy based on the Information Privacy Calculus Theory and Communication Privacy Management Theory. Design/methodology/approach This study made a design of the research model by integrating the factors deducted from the computation theory of information privacy with the factors deducted from the management theory of communication privacy on the basis of the Dual-Process Theory. Findings According to the empirical analysis result, this study confirmed that the Privacy Concern about forms through the Perceived Privacy Risk derived from the Disposition to value Privacy. In addition, this study confirmed that the behavior of an Internet user involved in personal information offering occurs due to the Perceived Benefits contradicting the Privacy Concern.

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<그림 1> RBM의 구조

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<그림 2> 딥러닝을 이용한 온라인 리뷰 기반 다속성별 추천 모형

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<그림 3> 토픽의 개수별 혼잡도

<표 1> 리뷰 기반의 추천시스템 연구

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<표 2> 다속성 기반 추천시스템 연구

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<표 3> 레스토랑 속성 및 키워드(일부)

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<표 4> 속성별로 분류된 리뷰 수

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<표 5> 속성별로 분류된 리뷰 결과(예시)

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<표 6> 레스토랑 속성별 데이터 요약

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<표 7> 실험 결과

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Supported by : 한국연구재단


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