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논문검색

반복 구매제품의 재구매시기 예측을 위한 다층퍼셉트론(MLP) 모형과 순환신경망(RNN) 모형의 성능비교

원문정보

Comparison of Performance between MLP and RNN Model to Predict Purchase Timing for Repurchase Product

송희석

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초록

영어

Existing studies for recommender have focused on recommending an appropriate item based on the customer preference. However, it has not yet been studied actively to recommend purchase timing for the repurchase product despite of its importance. This study aims to propose MLP and RNN models based on the only simple purchase history data to predict the timing of customer repurchase and compare performances in the perspective of prediction accuracy and quality. As an experiment result, RNN model showed outstanding performance compared to MLP model. The proposed model can be used to develop CRM system which can offer SMS or app based promotion to the customer at the right time. This model also can be used to increase sales for repurchase product business by balancing the level of order as well as inducing repurchase of customer.

목차

Abstract
 1. 서론
 2. 기존연구
 3. 순환신경망(RNN; Recurrent Neural Netwok)
  3.1 순환신경망의 구조와 작동원리
  3.2 순환신경망의 학습
  3.3 순환신경망의 한계
 4. 반복 구매제품의 재구매시기 예측모형구성
  4.1 신경망 구조 및 입력자료 설계
  4.2 모형의 구현
 5. 성능 평가
  5.1 실험용 데이터셋
  5.2 성능 평가지표 및 평가방법
  5.3 실험결과
 6. 결론 및 시사점
 References

저자정보

  • 송희석 Hee Seok Song. Department of Global IT Business in Hannam University

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