Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Development of Product Recommender System using Collaborative Filtering and Stacking Model

협업필터링과 스태킹 모형을 이용한 상품추천시스템 개발

  • Park, Sung-Jong (Department of Information and Statistics, Yonsei University) ;
  • Kim, Young-Min (Department of Bigdata Engineering, Soonchunhyang University) ;
  • Ahn, Jae-Joon (Department of Information and Statistics, Yonsei University)
  • 박성종 (연세대학교 정보통계학과) ;
  • 김영민 (순천향대학교 빅데이터공학과) ;
  • 안재준 (연세대학교 정보통계학과)
  • Received : 2019.05.16
  • Accepted : 2019.06.20
  • Published : 2019.06.28
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Abstract

People constantly strive for better choices. For this reason, recommender system has been developed since the early 1990s. In particular, collaborative filtering technique has shown excellent performance in the field of recommender systems, and research of recommender system using machine learning has been actively conducted. This study constructs recommender system using collaborative filtering and machine learning based on stacking model which is one of ensemble methods. The results of this study confirm that the recommender system with the stacking model is useful in aspects of recommender performance. In the future, the model proposed in this study is expected to help individuals or firms to make better choices.

사람들은 자신의 더 나은 선택을 위하여 끊임없이 노력한다. 이러한 이유로 추천시스템이 개발되었으며, 1990년대 초반부터 계속해서 발전하고 있다. 그 중, 협업필터링 기법은 추천시스템 분야에서 우수한 성능을 보였으며, 기계학습이 등장하면서 기계학습을 이용한 추천시스템에 관한 연구가 활발히 진행되었다. 본 연구는 앙상블 방법 중에서 스태킹 모형을 사용하여 추천시스템을 구축하며, 실제 고객의 상품 구매 데이터를 활용하여 협업필터링과 기계학습 기반 스태킹 모형으로 추천시스템을 개발하였다. 제시한 모형의 추천 성능은 기존의 협업필터링과 기계학습 기반 추천시스템과 비교하여 모형의 우수성을 확인하며, 연구결과는 스태킹 모형을 이용한 추천시스템 모형의 추천 성능이 개선됨을 확인하였다. 향후 본 연구에서 제안한 모형은 개인이나 기업이 더 나은 선택을 하여 상품을 추천할 때 도움을 줄 것으로 기대한다.

Keywords

JKOHBZ_2019_v9n6_83_f0001.png 이미지

Fig. 1. Example of new data set generated by stacking

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Fig. 2. Diagram of general stacking process

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Fig. 3. Flowchart of proposed stacking architecture

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Fig. 4. Misclassification rate(%) according to the number of neighbors in each window

Table 1. Example of confusion matrix

JKOHBZ_2019_v9n6_83_t0001.png 이미지

Table 2. Training period and test period assigned to each window

JKOHBZ_2019_v9n6_83_t0002.png 이미지

Table 3. Misclassification rate(%) of collaborative filtering when K value is under 20

JKOHBZ_2019_v9n6_83_t0003.png 이미지

Table 4. Misclassification rates(%) of proposed stacking model

JKOHBZ_2019_v9n6_83_t0004.png 이미지

Table 5. Misclassification rates(%) of machine learning classifiers without applying stacking model

JKOHBZ_2019_v9n6_83_t0005.png 이미지

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