역직구 상품 추천 및 판매가 추정을 위한 머신러닝 모델

• 영문명: Machine Learning Model for Recommending Products and Estimating Sales Prices of Reverse Direct Purchase
• 발행기관: 한국산업경영시스템학회
• 저자명: 김규익 볘르드바에브 예르갈리 김수형 김진석
• 간행물 정보: 『산업경영시스템학회지』제46권 제2호, 176~182쪽, 전체 7쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.06.30

영문 초록

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

목차

1. 서 론

2. 이론적 배경

3. 데이터 수집 및 전처리

4. 학습데이터 구축

5. 추천 모델 및 평가

6. 결론 및 향후 계획

Acknowledgement

References

키워드

Learning To Rank XGBoost Recommendation System