Journal of the Korea Society for Simulation (한국시뮬레이션학회논문지)

The Korea Society for Simulation (한국시뮬레이션학회)
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A Personalized Clothing Recommender System Based on the Algorithm for Mining Association Rules

연관 규칙 생성 알고리즘 기반의 개인화 의류 추천 시스템

  • 이종현 (고려대학교 컴퓨터.전파통신공학과) ;
  • 이석훈 (고려대학교 컴퓨터.전파통신공학과) ;
  • 김장원 (고려대학교 컴퓨터.전파통신공학과) ;
  • 백두권 (고려대학교 컴퓨터.전파통신공학과)
  • Received : 2010.07.03
  • Accepted : 2010.10.29
  • Published : 2010.12.31
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Abstract

We present a personalized clothing recommender system - one that mines association rules from transaction described in ontologies and infers a recommendation from the rules. The recommender system can forecast frequently changing trends of clothing using the Onto-Apriori algorithm, and it makes appropriate recommendations for each users possible through the inference marked as meta nodes. We simulates the rule generator and the inferential search engine of the system with focus on accuracy and efficiency, and our results validate the system.

이 논문에서는 온톨로지로 표현한 트랜잭션으로부터 연관 규칙을 생성하고 이를 기반으로 추론을 수행하여 개인화 의류 추천을 제공하는 시스템을 제안한다. Onto-Apriori 알고리즘을 이용한 연관 규칙 생성은 유행에 따른 구매성향 변동을 능동적으로 분석할 수 있다. 생성된 규칙은 온톨로지에 메타 노드로 표현하고 이를 기반으로 추론함으로써 사용자의 질의에 맞는 추천 항목을 찾아낼 수 있다. 시스템을 평가하기 위하여 추론 소요시간과 추천 정확도 2가지 요소를 기준으로 시뮬레이션을 수행하여 유효성을 증명하였다.

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