열람실 로그데이터를 활용한 좌석 유형 분류 및 물리적 요인에 관한 연구: K대학교를 중심으로

• 영문명: A Study on Seat Type Classification and Physical Factors Using Reading Room Log Data: Focusing on K University
• 발행기관: 한국IT서비스학회
• 저자명: 이순형(Sun Hyoung Lee) 김형진(Hyungjin Kim) 윤상혁(Sang-Hyeak Yoon)
• 간행물 정보: 『한국IT서비스학회지』제24권 제2호, 67~85쪽, 전체 19쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2025.04.30

국문 초록

University libraries are transforming into dynamic learning environments supporting academic pursuits and personal development. Although recent studies have employed various methods, most have primarily depended on subjective assessments or basic usage statistics, which do not fully capture user behavior through data-driven clustering techniques. This study utilized machine learning techniques to examine 157,021 library reading room usage logs. By applying K-means, BIRCH, and GMM algorithms, the research classified different seat types and analyzed environmental factors. Unlike previous studies, this research addressed methodological gaps by adopting clustering-based machine learning methods, enabling a systematic exploration of the relationship between seat preferences and environmental influences. The findings introduced a novel seat classification framework and an improved operational strategy, offering valuable contributions to the field of library science and practical insights for knowledge management. By leveraging advanced clustering techniques, this study represents a meaningful step forward in library research methodology, effectively linking theoretical insights with practical solutions for optimizing library space management.

목차

1. 서론

2. 개념적 배경

3. 연구방법론

4. 연구결과

5. 결과 토의 및 시사점

참고문헌

키워드

University Library Machine Learning Clustering Physical Factors Log Data