Korean Academic Society of Business Administration
[ Article ]
korean management review - Vol. 54, No. 1, pp.81-108
ISSN: 1226-1874 (Print)
Print publication date 28 Feb 2025
Received 13 Aug 2024 Revised 25 Nov 2024 Accepted 26 Dec 2024
DOI: https://doi.org/10.17287/kmr.2025.54.1.81

설명가능한 기계학습을 이용한 베스트셀러 예측과 영향요인 분석

이승필 ; 박은일 ; 류두진
(주저자)사회평론 전무이사
(공저자)성균관대학교 실감미디어공학과 부교수
(교신저자)성균관대학교 경제학과 교수
Predicting Bestsellers and Key Drivers using Explainable Machine Learning
Seungpeel Lee ; Eunil Park ; Doojin Ryu
(First Author)Executive Director, Sahoipyoungnon Publishing Co., Inc. leepeel@sapyoung.com
(Co-Author)Associate Professor, Department of Immersive Media Engineering, Sungkyunkwan University eunilpark@skku.edu
(Corresponding Author)Professor, Department of Economics, Sungkyunkwan University sharpjin@skku.edu


Copyright 2025 THE KOREAN ACADEMIC SOCIETY OF BUSINESS ADMINISTRATION
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly ited.

초록

온라인 서점의 방대한 데이터를 기반으로 판매량이나 베스트셀러를 예측하는 연구는 판매기록과 리뷰 등 출판 이후 자료에 의존하므로, 신간도서에 대해 베스트셀러 예측을 하기 어려운 콜드 스타트 문제를 가진다. 본 연구에서는 온라인 서점의 문학 장르에서 신간도서의 메타 데이터를 사용해 베스트셀러를 예측하는 기계학습 모형을 구현하였으며, LightGBM 모형이 가장 우수한 성능을 보였다. 특성 중요도 기법과 SHAP방법으로 저자 빈도, 출판사 빈도, 카테고리 빈도, 가격, 출판 월이 베스트셀러 예측에 영향을 미치는 요소임을 확인하였다. 연구 결과는 콜드 스타트 문제 해결에 기여하고, 온라인 서점이 신간도서의 성공 가능성을 예측하며 마케팅 전략을 수립하는 데 함의를 제공한다.

Abstract

Research on predicting sales volumes or identifying bestsellers in online bookstores often relies on post-publication data, such as sales records and customer reviews, which poses a cold start problem for new books. This study addresses this issue by developing a machine learning model based solely on metadata from newly released literary books. Among the tested models, LightGBM exhibits the best predictive performance. Using feature importance analysis and the SHAP method, we identify key factors influencing bestseller prediction, including author frequency, publisher frequency, category frequency, price, and publication month. Our findings provide a solution to the cold start problem and offer actionable insights for online bookstores to anticipate a book’s success potential and refine marketing strategies.

Keywords:

Bestseller, Explainable AI, Machine Learning, Online Bookstore, Prediction

키워드:

기계학습, 베스트셀러, 설명가능한 인공지능, 온라인 서점, 예측

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∙저자 이승필은 서울대 국사학과를 졸업하였으며, 성균관대 인공지능융합학과 대학원에서 공학 석사학위를 취득한 후 박사과정에 재학 중이다. 현재 (주)사회평론 출판사 전무이사로 재직 중이며, 한국도서출판정보센터 기술총괄위원, 성균관대 융합소프트웨어전공 겸임교수를 겸직하고 있다.

∙저자 박은일은 성균관대에서 전자전기컴퓨터공학, 인터랙션사이언스학으로 학사 및 석사학위를 수여받았으며, KAIST에서 사용자 혁신으로 박사학위를 수여받았다. 한국건설기술연구원과 한양대학교 ICT융합학부 교수를 거쳐 현재 성균관대학교 인공지능융합학과 부교수로 재직 중이다. 현재 Sustainable Development와 IEEE Transactions on Automation Science and Engineering과 같은 최우수 국제 학술지의 Associate Editor를 맡고 있으며, ICT혁신인재4.0사업단과 딥페이크 연구센터의 단장으로 활동하고 있다.

∙저자 류두진은 서울대 전기공학부를 졸업하였으며, KAIST 테크노경영대학원에서 경영공학 박사학위를 취득하였다. 국민연금공단 연구위원으로 근무했으며, 한국외대 국제경영학과 학과장과 중앙대 경제학부 교수를 거쳐 현재 성균관대 경제학과 교수이다. 한국경영학회 상임이사, 한국재무관리학회 부회장, 제23대 한국금융공학회 학회장을 지냈으며, 현재 한국경제학회 이사, 재무관리논총 편집위원장, 성균관대 경제연구소 소장, Global Finance Research Center 센터장을 담당하고 있다. Investment Analysts Journal (SSCI)의 Editor이며, Emerging Markets Review (SSCI)와 Journal of Multinational Financial Management (SSCI)의 Subject Editor이다.