• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.249-260
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• 1997

The manufacturing environment is rife with nonlinear processes. In this context, an intelligent production controller should be able to predict the dynamic behavior of various subsystems as they react to transient environmental conditions, the varying internal condition of the manufacturing plant, and the changing demands of the production schedule. This level of adaptive capability may be achieved through a coherent methodology for a learning coordinator to predict nonlinear and stochastic processes. The system is to serve as a real time, online supervisor for routine activities as well as exceptional conditions such as damage, failure, or other anomalies. The complexity inherent in a learning coordinator can be managed by a modular architecture incorporating case based reasoning. In the interest of concreteness, the concepts are presented through a case study involving a knowledge based robotic system.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.121-133
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• 2003

Due to the rapid growth of Internet, means of communication with customers in a traditional customer support environment such as telephone calls are being replaced by mainly e-mail in a Web-based customer support system. Although such a Web-based support is efficient and promises potential benefits for firms, including reduced transaction costs, reduced time, and high quality of support, there are some difficulties associated with responding to many types of customer's inbound e-mails appropriately. As many types of e-mail are received, considerable attention is being paid to methods for increasing the efficiency of managing and responding e-mails. This research proposes an intelligent system for managing customer's inbound e-mails in organizations by applying case based reasoning technique for responding to various customers' inbound e-mails more effectively. In this approach, a case is represented as a frame-typed data structure corresponding to an inbound e-mail, keywords, and its reply e-mail. In the retrieval procedure, keywords and affinity set is developed to index a case, and then the case is represented as a vector, a case vector. Also, cosines value is calculated to measure the similarity between a new inbound e-mail and the cases in the case base. In the adaptation procedure, we provide several adaptation strategies to adapt and modify the retrieved case. The strategies guide to make an outbound e-mail using product databases, databases for customer support, etc. Additionally, the Web-based system architecture is proposed to implement our methodology. The proposed methodology and system will be helpful for developing more efficient Web-based customer support.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.185-190
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• 2003

Due to the rapid growth of Internet, means of communication with customers in a traditional customer support environment such as telephone calls are being replaced by mainly e-mail in a Web-based customer support system. Although such a Web-based support is efficient and promises potential benefits for firms, including reduced transaction costs, reduced time, and high quality of support, there are some difficulties associated with responding to many types of customer’s inbound e-mails appropriately .As many types of e-mail are received, considerable attention is being paid to methods for increasing the efficiency of managing and responding e-mails. This research proposes an intelligent system for managing customer’s inbound e-mails in organizations by applying case based reasoning technique for responding to various customers' inbound e-mails more effectively. In this approach, a case is represented as a frame-typed data structure corresponding to an inbound e-mail, keywords, and its reply e-mail. In the retrieval procedure, keywords and affinity set is developed to index a case, and then the case is represented as a vector, a case vector. Also, cosines value is calculated to measure the similarity between a new inbound e-mail and the cases in the case base. In the adaptation procedure, we provide several adaptation strategies to adapt and modify the retrieved case. The strategies guide to make an outbound e-mail using product databases, databases for customer support, etc. Additionally, the Web-based system architecture is proposed to implement our methodology. The proposed methodology and system will be helpful for developing more efficient Web-based customer support.

• Proceedings of the Korea Database Society Conference
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• 1997.10a
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• pp.215-269
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• 1997

Outline $\textbullet$ Introduction $\textbullet$ Multimedia - Types of Data - Motivation - Key issue - Hardware Products - Application Areas $\textbullet$ Agents - Rationale for Agents - Sedentary vs. Mobile - Functional Categories - Application Areas $\textbullet$ Data Mining - 2-D Framework for Data Mining Tools - Classification of Tool - Application Areas - Learning Methodologies ＊ Case Based Reasoning ＊ Neural Networks ＊ Statistical Learning： Orthogonal Arrays ＊ Multi-strategy Learning $\textbullet$ Case Study - Finbot $\textbullet$ Conclusion

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1095-1106
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• 2006

Recently, many effort is demanded for appropriate operation and management of the Electronic commerce system. Electronic commerce system must provide information satisfying customers. To do this, many kinds of studies are being advanced about electronic commerce system using intelligent agent technology. In this paper, We propose a support system for goods based on Case-Based Reasoning(CBR) and multi-crossselling technology. The system can supply the information of goods that is suitable about customer need under specification condition with CBR technique and can search several goods approximate to customer pattern using Multi-crossselling technique.

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.108-118
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• 2010

In this paper, we propose Context-based Music Recommendation (COMUS) ontology for modeling user's musical preferences and context and for supporting reasoning about the user's desired emotion and preferences. The COMUS provides an upper Music Ontology that captures concepts about the general properties of music such as title, artists and genre and also provides extensibility for adding domain-specific ontologies, such as Mood and Situation, in a hierarchical manner. The COMUS is music dedicated ontology in OWL constructed by incorporating domain specific classes for music recommendation into the Music Ontology. Using this context ontology, we believe that the use of logical reasoning by checking the consistency of context information, and reasoning over the high-level, implicit context from the low-level, explicit information. As a novelty, our ontology can express detailed and complicated relations among the music, moods and situations, enabling users to find appropriate music for the application. We present some of the experiments we performed as a case-study for music recommendation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.56-59
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• 1998

The rapid diffusion of the Internet and the World Wide Web is facilitating greater interaction - and potential opportunities for customization - between vendors and customers. For instance, virtual reality and multimedia technologies allow consumers to envision their designed products. The increasing pace of the business environment highlights the need to deploy intelligent systems in all stages of product design and production planning. This paper deals with a system to generate an appropriate product design by adapting customer preferences and constraints using the case based reasoning methodology. Moreover, intelligent agents are integrated with virtual reality technology to provide a friendly user interface.

• Journal of Information Technology Applications and Management
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• v.12 no.3
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• pp.41-56
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• 2005

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.49-55
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• 2019

In this paper, we propose ontology-based recommendation system for supporting maintenance of Korean architectural heritage. This study includes the following: 1) design of ontology expressing repair information of architectural heritage, 2) creation of repair case DB, 3) creation of a recommendation system of repair method. For this study, we designed the ontology that expresses the information of Korean wooden building cultural heritage by referring to the existing heritage ontologies. Second, we created the repair information database based on the repair contents and the expert interview data provided by the National Institute of Cultural Heritage and the Cultural Heritage Administration. Third, we developed a system that recommends the repair method of Korean wooden architectural heritage with the most similar phenomena and causes. This study contributes to sharing repair knowledge and determining repair methods for architectural heritage repair.