본 연구에서는 국내 초고층 건물 사례들을 바탕으로 사례 데이터베이스를 구축하여 구조시스템 대안 생성 및 선정 업무를 효율적으로 처리 할 수 있는 설계지원시스템을 개발하였으며, 사례 데이터베이스의 정보를 이용한 사례기반추론기법을 제안하였다. 국내 47개 초고층 건물에 대한 사례 데이터베이스의 설계정보를 분석하여 초기설계 단계에서 구조시스템 선정을 위한 귀납적 조회 모듈 및 유사사례선정을 위한 최근린 조회기법도 제시하였다.

This study suggests an alternative to the conventional collaborative filtering method for predicting consumer choice, using case-based reasoning. The algorithm of case-based reasoning determines the similarity between the alternative sets that each subject chooses. Case-based reasoning uses the inverse of the normalized Euclidian distance as a similarity measurement. This normalized distance is calculated by the ratio of difference between each attribute level relative to the maximum range between the lowest and highest level. The alternative case-based reasoning based on similarity predicts a target subject’s choice by applying the utility values of the subjects most similar to the target subject to calculate the utility of the profiles that the target subject chooses. This approach assumes that subjects who deliberate in a similar alternative set may have similar preferences for each attribute level in decision making. The result shows the similarity between comparable alternatives the consumers consider buying is a significant factor to predict the consumer choice. Also the interaction effect has a positive influence on the predictive accuracy. This implies the consumers who looked into the same alternatives can probably pick up the same product at the end. The suggested alternative requires fewer predictors than conjoint analysis for predicting customer choices.