Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Customer Recommendation Using Customer Preference Estimation Model and Collaborative Filtering

선호도 추정모형과 협업 필터링기법을 이용한 고객추천시스템

  • Shin, Taeksoo (Dept. of Management Information Systems, College of Government and Business, Yonsei University) ;
  • Chang, Kun-Nyeong (Dept. of Management Information Systems, College of Government and Business, Yonsei University) ;
  • Park, Youjin (Dept. of Business Administration, The Graduate School, Yonsei University)
  • 신택수 (연세대학교 정경대학 경영학부) ;
  • 장근녕 (연세대학교 정경대학 경영학부) ;
  • 박유진 (연세대학교 정경대학 경영학부)
  • Published : 2006.12.31
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Abstract

This study proposed a customer preference estimation model for production recommendation and a method to enhance the performance of product recommendation using the estimated customer preference information. That is, we suggested customer preference estimation model to estimate exactly customer's product preference with his behavior. This model shows the relationship of customer's behaviors with his preferences. The proposed estimation model is optimized by learning the relative weights of customer's behavior variables to have an effect on his preference and enables to estimate exactly his preference. To validate our proposed models, we collected virtual book store data and then made a comparative analysis of our proposed models and a benchmark model in terms of performance results of collaborative filtering for product recommendation. The benchmark model means a prior preference weighting model. The results of our empirical analysis showed that our proposed model performed better results than the benchmark model.

본 연구는 상품추천을 위해 필요한 고객 선호도 추정모형(Customer Preference Estimation Model)을 제안하고, 이러한 선호도 추정결과에 따른 선호도 정보를 이용하여 궁극적으로 상품추천의 성과를 제고시키기 위한 방법을 제시하였다. 즉, 제품에 대한 고객 선호 영향요인들과 고객 선호도와의 관계를 모형화 함으로써 고객 선호도를 보다 더 정확히 추정할 수 있는 새로운 선호도 추정모형을 제안하였다. 이 제안모형은 선호도 영향요인들의 상대적인 가중치를 선호도 최적화 학습을 통해 도출함으로써, 보다 정확한 선호도 측정을 가능하게 해 준다. 한편, 이 모형의 타당성을 검증하기 위해서 본 연구에서는 가상서점 고객들을 대상으로 고객 선호도 정보를 수집한 후, 본 제안모형을 적용했을 때의 협업 필터링의 추천성과와 사전가중치 부여방식인 기존 선호도 계산식을 이용했을 경우의 추천성과를 비교 분석하였다. 이에 대한 실증분석 결과는 본 연구에서 제안한 선호도 추정모형을 적용했을 때의 협업 필터링의 성과가 기존 선호도 계산방식을 적용했을 때의 협업 필터링의 성과보다 더 우수한 것으로 나타났다.

Keywords

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