Customized Exhibition Environment for the National Museum

Overview

While the demand for leisure activities has increased with the emphasis on work-life balance, the digital transformation of art museum operations—a core pillar of civic leisure and culture—remains insufficient. Identifying visitor characteristics is essential for providing customized exhibition and educational content; however, even representative institutions like the National Museum of Modern and Contemporary Art (MMCA) lack sophisticated customer management systems that reflect granular visitor traits. This situation is even more challenging for smaller public and private museums. To address these gaps, this project aimed to develop comprehensive APIs that can scientifically track and utilize visitor data.

Project description

The project focused on the development and release of three core API categories. First, we established the Visitor Statistics API per Exhibition, which provides detailed statistics for each exhibition at MMCA by utilizing telecommunications data (LG Uplus) and CCTV analysis data. Second, the Visitor Trend Statistics API was developed in conjunction with telecommunications data to aggregate statistics based on detailed visitor profiles, including gender, age, household type, lifestyle-based movement patterns, and preferred media content types. Finally, we developed the Personalized Exhibition Recommendation API, which generates tailored exhibition lists by considering the specific tastes and living radius of the users. These APIs are designed to be open for wider institutional use.

Key technological features

• Personalized Exhibition Recommendation Service based on AI Algorithms: Providing tailored exhibition recommendations by analyzing user preferences and behavior
• Development APIs: Developing and releasing APIs for visitor trends, household trends, demographics (gender/age), visits by day and time, integrated visitor data, and customized exhibition recommendation data.

Expected outcomes

By scientifically collecting and analyzing data on viewing patterns and exhibition environments, this service fosters a personalized cultural experience, which is expected to significantly enhance public cultural welfare and attract new audiences. This data-driven approach will drive service innovation to satisfy the public's cultural needs. Furthermore, by supporting evidence-based decision-making for museum policies, the system will help secure the validity of national museum operations, revitalize museum management, and contribute to the sustainable qualitative and quantitative growth of national cultural institutions in the long term.

Launch year: 2023

Attribution

Case study courtesy of the National Information Society Agency, Republic of Korea, Apolitical's Content Partner. Sourced from Use Cases of Public AI Service, Vol.2, https://eng.nia.or.kr/site/nia_eng/ex/bbs/View.do?cbIdx=31975&bcIdx=29182&parentSeq=29214