[논문]전자상거래 이용시 연관성 분석을 통한 맞춤형 상품추천 모델 설계

박기용; 최상현

doi:10.15207/jkcs.2022.13.03.203

전자상거래 이용시 연관성 분석을 통한 맞춤형 상품추천 모델 설계
Design of customized product recommendation model on correlation analysis when using electronic commerce 원문보기

한국융합학회논문지 = Journal of the Korea Convergence Society, v.13 no.3, 2022년, pp.203 - 216

(충북대학교 경영정보학과) , 박기용 (충북대학교 빅데이터협동과정) , 최상현 (충북대학교 빅데이터협동과정)

초록
AI-Helper

본 연구에서는 COVID-19의 영향과 온라인 시장을 중심으로 구매패턴이 변화하는 현 경영환경의 시대에서 온라인 배송업체의 구매정보와 상품정보를 기반으로 군집분석과 연관성 분석을 실시하였다. 고객군집, 상품군집, 그리고 교차결합을 통해 데이터를 세분화시켜 결합군집을 생성하여 학문적으로 새로운 방안의 군집분석을 시도하였으며, 각각의 군집분석 결과를 토대로 연관성 분석을 하였다. 연관성 분석 결과, 상대적으로 결합군집에서 더 많은 연관 규칙이 도출 되었으며, 중복률은 더 적은 것으로 분석되어 효율성이 매우 높은 것으로 나타났다. 이는 고객의 니즈에 맞게 상품을 추천하기 위해서는 결합군집이 가장 적합한 모델이라고 판단된다. 결합군집 모델은 소비자에겐 시간 절약과 유용한 정보를 제공하면서, 해당 업체에는 판매량을 증가시키는 등의 긍정적인 효과를 가져올 것으로 사료된다. 향후 연구과제로써, 다양한 특성을 갖고 있는 다수의 온라인 배송업체들을 대상으로 비교·분석한다면 좀 더 명확하고 유의미한 연구결과를 도출할 수 있을것으로 기대된다.

Abstract ▼ AI-Helper

In the recent business environment, purchase patterns are changing around the influence of COVID-19 and the online market. This study analyzed cluster and correlation analysis based on purchase and product information. The cluster analysis of new methods was attempted by creating customer, product, and cross-bonding clusters. The cross-bonding cluster analysis was performed based on the results of each cluster analysis. As a result of the correlation analysis, it was analyzed that more association rules were derived from a cross-bonding cluster, and the overlap rate was less. The cross-bonding cluster was found to be highly efficient. The cross-bonding cluster is the most suitable model for recommending products according to customer needs. The cross-bonding cluster model can save time and provide useful information to consumers. It is expected to bring positive effects such as increasing sales for the company.

주제어

표/그림 (15)

그림 Fig. 1. COVID-19 confirmed cases in Korea(2020) Source: https://coronaboard.kr/
표 Table 1. Customer and transaction information items
표 Table 2. Integrated information items
표 Table 3. Transaction information according to classification
표 Table 4. Transaction information by customer age group
그림 Fig. 2. Monthly usage of 'Company M' (2020)
그림 Fig. 3. Analysis number of customer clusters
그림 Fig. 4. Analysis number of product clusters
표 Table 5. Basic statistical analysis of customer clusters
표 Table 6. Basic statistical analysis of product clusters
표 Table 7. Basic statistical analysis of cross-bonding clusters
표 Table 8. Association rules by customer cluster
표 Table 9. Association rules by product cluster
표 Table 10. Association rules by cross-bonding cluster
표 Table 10. (Continued)

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