유통기업 판매상품 추천 알고리즘 실증연구
A Study of Product Recommendation Algorithms for Retailers
- 발행
- 2025 등록KCI API에는 발행일 항목이 없어 원문 등록일을 쓰고 있습니다. 실측으로 발행보다 최대 6년 늦습니다 — 재수집하면 발행연월로 바뀝니다.
- 소속·발행
- 한국전자통신연구원 우정․물류기술 연구센터
- 출처
- 국내 KCI
- DOI
- 10.11627/jksie.2025.48.2.190
- 원문
- 원문 보기 ↗
개념
키워드
Product Recommendation Model, Deep Learning, Ensemble Model, Small-Scale Retailers, Product Recommendation Model, Deep Learning, Ensemble Model, Small-Scale Retailers
초록
The expansion of online retail markets has driven the development of personalized product recommendation services leveraging platform-based product and customer data. Large retailers have implemented seller-oriented recommendation systems, where AI analyzes POS sales data to identify similar stores and recommend products not yet introduced but successful elsewhere. However, small and medium-sized retailers face challenges in adapting to rapidly evolving online market trends due to limited resources. This study proposes a recommendation algorithm tailored for small-scale retailers using sales data from an online shopping mall. We analyzed 600,000 transaction records from 13,607 sellers and 95,938 products, focusing on Beauty Supplies, Kitchenware, and Cleaning Supplies categories. Three algorithms—Attentional Factorization Machines (AFM), Deep Factorization Machines (DeepFM), and Neural Collaborative Filtering (NCF)—were applied to recommend top 10% weekly sales items, with an ensemble model integrating their strengths. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was employed, and performance was evaluated using AUC, Accuracy, Precision, and Rec