Earticle

초록

영어

This study used machine learning to derive results on how the evaluation of each function of objective consumers affects repurchase behavior. An analysis model using SVM, Rondom Forest, Decision Tree, and Neural Network, which are currently under study, was proposed, and collected reviews were divided into two categories to explore not only the leading factors of repurchase behavior but also the correlation factors. The model presented in this study confirmed that real-time collected data can be used immediately online, and that detailed positives and negatives of consumer reviews can be identified, so that user responses can be collected and utilized for decision making. In other words, it showed that practical use is possible through four algorithms: SVM, Rondom Forest, Decision Tree, and Neural Network based on machine learning. Through this, it proposed a way to use datamining- based machine learning techniques instead of collecting data through questionnaires in consumer behavior analysis.It also provided marketers with rationale and strategic implications because it could identify individual repurchase behaviors of consumers.

목차

Abstract
서론 머신러닝 실행을 위한 선행 자료
머신러닝 실행을 위한 선행 자료
1. 사례선정
2. 상품후기
3. 재구매행동
4. 머신러닝의 활용
연구설계
1. 변수 선정
2. 재구매행동 측정방법
연구방법및분석
1. 연구 방법
2. 분석 결과
References

키워드

저자

• WANGSHUANG [ 전남대학교 디지털미래용합서비스학과 ]
• 윤현식 [ 전남대학교 경영대학 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658