• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1537-1542
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• 2004

Various studies to increase customer satisfaction of a web based system are performed actively. Also in recent days an interest about the personalization that supporting a order type service on customer's viewpoint was raised. So the studies supporting the personalization is required in a web-based marketing system. In this study, we designed an intelligent recommendation system which supporting one to one web marketing using cross selling. The proposed system used an intelligent data mining method as a concurrent cross selling and a sequential cross selling. Also, In experiment on the prototype, we show a proposed system was usable in an practical system applying the mining result.

• Journal of the Korean Society for information Management
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• v.23 no.4 s.62
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• pp.179-196
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• 2006

This study proposes an expanded model of personalized search service based on community activities on a Korean Web portal. The model is composed of defining subject categories of users, providing personalized search results, and recommending additional subject categories and queries. Several experiments were performed to verify the feasibility and effectiveness of the proposed model. It was found that users' activities on community services provide valuable data for identifying their Interests, and the personalized search service increases users' satisfaction.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.319-328
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• 2003

With the proliferation of WWW, providing more intelligence to Web sites has become a major concern in e-business industry. In recent days, this trend is more accelerated by prosperity of CRM (Customer Relationship Management) in terms of various aspects such as product recommendation, self after service, etc. To accomplish this goal, many e-companies are eager to embed web enabled rule-based system, that is, expert systems into their Web sites and several well-known commercial tools are already available in the market. Most of those tools are developed based on CGI so far but CGI based systems inherently suffer over-burden problem when there are too many service demands at the same time due to the nature of CGI. To overcome this limitation of the existing CGI based expert systems, we propose a new form of Web-enabled expert system using hyperlink-based inference mechanism. In terms of burden to Web server, our approach is proven to outperform CGI based approach theoretically and also empirically. For practical purpose, our this approach is implemented in a software system, WeBIS and a graphic rule editing methodology, Expert Diagram is incorporated into the system to facilitates rule generation and maintenance. WeBIS is now successfully operated for financial consulting in the web site of a leading financial consulting company in Korea.

• Journal of the Ergonomics Society of Korea
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• v.31 no.1
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• pp.211-217
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• 2012

Objective: The aim of this study is to show a correct application procedure using the compact Korean anthropometric data application program called Korean Human Scale(KHS) for anthropometric design. Background: The nation-wide anthropometric survey project called 'Size Korea' developed KHS and distributed it to the public on the web site. But some insufficiency of the current web service of KHS misleads the users; they just put their own statue and pick up a meaningless data for a body dimension. Method: This study provides five steps to follow to read appropriate data from KHS for an anthropometric design. Results: As a case study, the depth dimension of the supervisory and control console used in the Korea nuclear power plant was determined following the procedure and compared with the console design guideline recommendation. Conclusion: The supplementary anthropometry table should be added on the web service of KHS for users to read a meaningful data for design. Application: If properly used, the KHS has a lot more potential application area than users can expect such as in control center design area.

• Science of Emotion and Sensibility
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• v.9 no.spc3
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• pp.179-185
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• 2006

This study examines and compares various recommendation techniques which have been used successfully in other fields and seeks for opportunity to improve design personalization service more effectively. Throughout the literature study, several major recommendation techniques were identified, namely 'contents-based filtering', 'collaborative filtering', and 'demographic filtering'. In order for finding out relative advantages and disadvantages, a case study was carried out by applying different techniques. The result showed that in general, demographic filtering was evaluated least efficient among the techniques. Content-based filtering showed the best efficiency among them. Another significant finding was that the collaborative filtering had a better efficiency as the number of test subjects is increased. In conclusion, we suggest that design recommendation services can be improved by applying contents-based or collaborative filtering for better efficiency of recommendation. And, if the number of test subjects is large enough, it may be possible to remarkably improve the efficiency of design recommendation services by using collaborative filtering.

• Archives of design research
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• v.16 no.2
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• pp.23-34
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• 2003

The need for personalized web site design has been increased these days. Current approach for personalized web site design is easily applied to web site with their cost-effective feature, but is hard to provide a more refined personalized service due to its lack of accumulation of user data. In this study, the design recommender system is investigated as a more advanced method for web site design personalization. We provide an overview of current recommender systems, and then outlined a newly developed design recommender system, which employs collaborative filtering technique to provide tailored recommendation for users.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.377-379
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• 2022

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.71-80
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• 2006

As e-commerce developing rapidly, it is becoming a research focus about how to find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. One of the most successful and widely used technologies for building personalization and goods recommendation system is collaborating filtering. However, collaborative filtering have serious data sparsity problem. Traditional association rule does not consider user's interests or preferences to provide a user with specific personalized service.In this paper, we propose an goods recommendation system, which is integrated an collaborative filtering algorithm with item-to-item corelation and an improved Apriori algorithm. This system has user's interests or preferences ro provide a user with specific personalized service.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.43-48
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• 2008

And this study intends to discuss especially the one using emotional filter among various information processing methods. The existing music recommendation service on the web has a weak point that it makes the user feel bored by recommending songs only with similar feeling of the same genre, because music is classified by tune, melody, atmosphere and genre before recommendation. The service using emotion filter, suggested in this study, recommends the song and lyrics appropriate to the current emotional state of the user by abstracting emotional words that could reflect the sensitivity of human and then search the words within lyrics to match in order to overcome the weak point of the existing service. This study starts where the current emotional status for the user is being input. As for the range to choose, there are the seven representatives of emotion which are, love, separation, joy, sorrow-gloom, happiness-lonesome, and anger. As the service receives input of user's emotion, it matches the emotional words appropriate for the emotion input with the lyrics, and ranks the lyrics in the order of priority, so that it recommends the song and it lyrics to the user.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.7-17
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• 2021

This study investigated the effect of size recommendation technology convergence on purchase intention mediated by information credibility and satisfaction. The survey for this study was conducted on Amazon Mechanical Turk targeting U. S. residing women aged 18 to 60 years old who have never used size recommendation technology. They experienced the size recommendation technology in the provided web page and returned to the survey to answer the questionnaire. The analysis was done with 213 surveys using SPSS 27.0 and Process Macro (model 6, 5,000 Bootstrapping sample). The dimensions of service quality were found to be responsiveness and ease of use, and both have a significant effect on purchase intention through information credibility and satisfaction.