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Machine Learning Model for Recommending Products and Estimating Sales Prices of Reverse Direct Purchase KCI 등재
역직구 상품 추천 및 판매가 추정을 위한 머신러닝 모델
- 언어KOR
- URLhttps://db.koreascholar.com/Article/Detail/423152
pp.176-182
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.
2. 이론적 배경
2.1 상품추천 모델
2.2 판매가격 추정 모델
3. 데이터 수집 및 전처리
3.1 데이터 수집
3.2 데이터 통합(Integration)
3.3 데이터 변환(Transformation)
3.4 데이터 정제(Cleaning)
4. 학습데이터 구축
4.1 데이터 상관분석(Correlation Analysis)
4.2 특징 추출(Feature Extraction)
5. 추천 모델 및 평가
5.1 상품 추천 모델
5.2 상품 추천 모델 평가
5.3 판매가격 추정 모델
5.4 판매가격 추정 모델 평가
5. 결론 및 향후 계획
Acknowledgement
References
