Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
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Non-hierarchical Clustering based Hybrid Recommendation using Context Knowledge

상황 지식을 이용한 비계층적 군집 기반 하이브리드 추천

  • Baek, Ji-Won (Department of Computer Science, Kyonggi University) ;
  • Kim, Min-Jeong (Department of Computer Science, Kyonggi University) ;
  • Park, Roy C. (Department of Information Communication Engineering, Sangji University) ;
  • Jung, Hoill (Department of Computer Software, Daelim University) ;
  • Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
  • 백지원 (경기대학교 컴퓨터과학과) ;
  • 김민정 (경기대학교 컴퓨터과학과) ;
  • 박찬홍 (상지대학교 정보통신공학과) ;
  • 정호일 (대림대학교 컴퓨터소프트웨어과) ;
  • 정경용 (경기대학교 컴퓨터공학부)
  • Received : 2019.09.11
  • Accepted : 2019.09.30
  • Published : 2019.09.30
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Abstract

In a modern society, people are concerned seriously about their travel destinations depending on time, economic problem. In this paper, we propose an non-hierarchical clustering based hybrid recommendation using context knowledge. The proposed method is personalized way of recommended knowledge about preferred travel places according to the user's location, place, and weather. Based on 14 attributes from the data collected through the survey, users with similar characteristics are grouped using a non-hierarchical clustering based hybrid recommendation. This makes more accurate recommendation by weighting implicit and explicit data. The users can be recommended a preferred travel destination without spending unnecessary time. The performance evaluation uses accuracy, recall, F-measure. The evaluation result was shown 0.636 accuracy, 0.723 recall, and 0.676 F-measure.

현대 사회에서 사람들은 시간적인 여유, 경제적인 문제 등에 따라 여행지에 대해 심각한 고민을 한다. 따라서 본 논문에서는 상황 지식을 이용한 비계층적 군집 기반 하이브리드 추천을 제안한다. 제안하는 방법은 사용자의 위치, 장소, 날씨 등의 상황에 따라 선호하는 여행지에 대한 지식을 추천받을 수 있는 개인화된 방법이다. 설문조사를 통해 수집된 데이터로부터 14개의 속성을 기반으로 유사한 특성을 가진 사용자들을 비계층적 군집 기반 하이브리드 추천을 이용하여 군집한다. 이는 암묵적 데이터와 명시적 데이터에 가중치를 부여하여 보다 정확한 추천을 한다. 이를 통해 사용자는 불필요한 시간을 소모하지 않고 선호하는 여행지를 추천받을 수 있다. 성능평가는 정확도, 재현율, F-measure를 이용한다. 평가 결과 정확도는 0.636, 재현율은 0.723, F-measure는 0.676으로 평가되었다.

Keywords

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