Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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A Movie Recommendation System processing High-Dimensional Data with Fuzzy-AHP and Fuzzy Association Rules

퍼지 AHP와 퍼지 연관규칙을 이용하여 고차원 데이터를 처리하는 영화 추천 시스템

  • Oh, Jae-Taek (Department of Computer Science & Engineering, Kongju National University) ;
  • Lee, Sang-Yong (Division of Computer Science & Engineering, Kongju National University)
  • 오재택 (공주대학교 컴퓨터공학과) ;
  • 이상용 (공주대학교 컴퓨터공학부)
  • Received : 2018.11.19
  • Accepted : 2019.02.20
  • Published : 2019.02.28
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Abstract

Recent recommendation systems are developing toward the utilization of high-dimensional data. However, high-dimensional data can increase algorithm complexity by expanding dimensions and be lower the accuracy of recommended items. In addition, it can cause the problem of data sparsity and make it difficult to provide users with proper recommended items. This study proposed an algorithm that classify users' subjective data with objective criteria with fuzzy-AHP and make use of rules with repetitive patterns through fuzzy association rules. Trying to check how problems with high-dimensional data would be mitigated by the algorithm, we performed 5-fold cross validation according to the changing number of users. The results show that the algorithm-applied system recorded accuracy that was 12.5% higher than that of the fuzzy-AHP-applied system and mitigated the problem of data sparsity.

최근 추천 시스템들은 고차원 데이터를 사용할 수 있는 시스템으로 발전하고 있다. 그러나 고차원 데이터는 차원을 확장시켜 알고리즘 복잡도가 증가하여 추천 항목의 정확도를 저하시킬 수 있다. 또한 데이터의 희소성(Sparsity) 문제가 발생할 수 있어 사용자들에게 적합한 추천 항목을 제공하는 것이 어렵다. 본 연구에서는 Fuzzy-AHP를 이용하여 사용자들의 주관적 기준의 데이터를 객관적 기준으로 분류한 후, 퍼지 연관규칙 분석을 이용하여 반복적 패턴을 띄는 규칙들을 활용하는 알고리즘을 제안하였다. 본 연구에서 적용된 알고리즘이 고차원 데이터의 문제점들을 어떻게 완화하는지 확인하기 위해 사용자 수의 변화에 따른 5-fold Cross Validation을 진행하였다. 그 결과 본 알고리즘이 적용된 시스템의 정확도는 Fuzzy-AHP만을 적용한 시스템보다 12.5% 정도 정확도가 우수하였고, 데이터의 희소성 문제도 완화할 수 있다는 것을 확인하였다.

Keywords

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Fig. 1. System Structure

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Fig. 2. AHP Structure of Input Module

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Fig. 3. Entry Forms of User Preference

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Fig. 4. [Program Capture] User Interface of Input Module

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Fig. 5. [Program Capture] User Interface of Recommendation Module

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Fig. 6. Investigation of User Preference based on Pairwise Comparison

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Fig. 7. 5-fold Cross Validation According to the Changing Number of Users

Table 1. Development Environment

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Table 2. Classification of Preference based on Triangular Fuzzy Numbers

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