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.

Abstract
1. Introduction
2. Literature Review
    2.1. Prediction of Consumer’s Choice by CasebasedReasoning
    2.2. Similarity Evaluation
3. Research Methods
    3.1. Procedure
    3.2. Stimuli for Experiment
4. Empirical Study
    4.1. Data Collection
    4.2. Prediction Algorithm
    4.3. Results of Simulation
    4.4. Discussion
5. Conclusion
    5.1. Summary
    5.2. Limitations and Further Research
References

• Sang Yun SEO(Department of Business Administration, Kyungnam University)
• Sang Duck KIM(Department of Business Administration, Kyungnam University)
• Seong Chan JO(School of Management, Kyunghee University) Corresponding Author