• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.227-235
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• 2010

This paper proposes the recommendation system which is advanced using RFM method and Association Rules in e-Commerce. Using a implicit method which is not used user's profile for rating, it is necessary for user to keep the RFM score and Association Rules about users and items based on the whole purchased data in order to recommend the items. This proposing system is possible to advance recommendation system using RFM method and Association Rules for cross-selling, and also this system can avoid the duplicated recommendation by the cross comparison with having recommended items before. And also, it's efficient for them to build the strategy for marketing and crm(customer relationship management). It can be improved and evaluated according to the criteria of logicality through the experiment with dataset collected in a cosmetic cyber shopping mall. Finally, it is able to realize the personalized recommendation system for one to one web marketing in e-Commerce.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.07a
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• pp.171-172
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• 2015

Vinalines is actually known as not only one of Vietnam's state-sponsored economic giants but also the largest shipowner by tonnage in Vietnamese shipping industry. Therefore, a question of how to improve business performance of the corporation is always received deep attention by Vietnamese government, specially after the seriously economic scandal of Vinalines in a last few years. Among development strategies, the study focuses on short-term one in which Vinalines is recommended to restructure its own fleet in order to optimize performance of fleet operation and minimize costs while meeting the customer's shipping demand in near future. The first section is of introduction. Via method of statistical data analysis, section 2 brings to readers a panorama about the development profile and the current situation of development of Vinalines. In section 3, the authors use linear programming for setting a cost-minimization model optimizing Vinalines fleet structure based on available statistics and forecast information by Vinalines. The optimization problem is solved by applying AIMMS software in section 4. Finally, some conclusions and proposals by authors for the development of Vinalines are given.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.204-209
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• 2002

The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of the Korean Society of Food Culture
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• v.24 no.6
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• pp.655-665
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• 2009

Coupled with the international expansion of Korean culture in recent years, a number of restaurants from Korea have been trying to tap into the global market place. The purpose of this study was to identify the characteristics of non-Korean patrons in Korean Restaurants in New-york city. The survey was conducted at six popular Korean restaurants, all of which had been recognized in the Zagat Survey in recent years, located in prime business districts in Manhattan. The data collected from the six local Korean restaurants that participated in this study were qualitatively and quantitatively analyzed. After employing individual in-depth interviews with restaurant operators, a qualitative analysis identified demographic characteristics, Socioeconomic characteristics and segmentation of restaurant operation. Self-administrated survey questionnaires were used to acquire quantitative data. Primary data were collected from non-Korean patrons at the six participating Korean restaurants in New York City in 2008 (N=245). The patrons who answered the survey indicated that they were highly satisfied with the 'Food'; however, they were not satisfied with the 'Beverage' and 'Value'. In addition, older patrons (55<) were not as content with the 'Food' as the younger patrons. The most influential satisfaction variable that affected a patron's intention to revisit the Korean restaurant was 'Food' and 'Overall experience'. This study findings will help Korean restaurant operators and marketers better understand their patrons and formulate strategies to cater and target segments more effectively.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.1-20
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• 2008

Based on the multi-attribute utility theory (MAUT), we present a survey method to measure consumer preferences. The multi-attribute utility theory has been used to make decisions in OR/MS field; however, we show that the method can be effectively used to estimate the demand for new services by measuring individual level utility function. Because conjoint method has been widely used to measure consumer preferences for new products and services, we compare the pros and cons of two consumer preference survey methods. Further, we illustrate how swing weighing method can be effectively used to elicit customer preferences especially for new telecommunications services, Multi-attribute utility theory is a compositional approach for modeling customer preference, in which researchers calculate overall service utility by summing up the evaluation results for each attribute. On the contrary, conjoint method is a decompositional approach, which requires holistic evaluations for profiles. Partworth for each attribute is derived or estimated based on the evaluation, and finally consumer preferences for each profile are calculated. However, if the profiles are quite new and unfamiliar to the survey respondents, they will find it very difficult to accurately evaluate the profiles. We believe that the multi-attribute utility theory-based survey method is more appropriate than the conjoint method, because respondents only need to assess attribute level preferences and not holistic assessment. We chose swing weighting method among many weight assessment methods in multi-attribute utility theory, because it is designed to perform in a simple and fast manner. As illustrated in Clemen and Reilly (2001), to assess swing weights, the first step is to create the worst possible outcome as a benchmark by setting the worst level on each of the attributes. Then, each of the succeeding rows "swings" one of the attributes from worst to best. Upon constructing the swing table, respondents rank order the outcomes (rows). The next step is to rate the outcomes in which the rating for the benchmark is set to be 0 and the rating for the best outcome to be 100, and the ratings for other outcomes are determined in the ranges between 0 and 100. In calculating weight for each attribute, ratings are normalized by the total sum of all ratings. To demonstrate the applicability of the approach, we elicited and analyzed individual-level customer preference for new telecommunication services-WiBro and HSDPA. We began with a randomly selected 800 interviewees, and reduced them to 432 because other remaining ones were related to the people who did not show strong intention for subscription to new telecommunications services. For each combination of content and handset, number of responses which favored WiBro and HSDPA were counted, respectively. It was assumed that interviewee favors a specific service when expected utility is greater than that of competing service(s). Then, the market share of each service was calculated by normalizing the total number of responses which preferred each service. Holistic evaluation of new and unfamiliar service is a tough challenge for survey respondents. We have developed a simple and easy method to assess individual level preference by estimating weight of each attribute. Swing method was applied for this purpose. We believe that estimating individual level preference will be quite flexibly used to predict market performance of new services in many different business environments.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.3
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• pp.73-80
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• 2018

The growth of Bandung's tourism industry has had a massive impact on the hotel sector. Most tourists visiting Bandung are domestic tourists and tend to be modest spenders fitting the profile of a mid-market (2/3 star) hotel guest. As competition has increased mid-market hotels have come under pressure from upmarket (4/5 star) and budget hotels committed to cutting prices. There is also competition with the mid-market hotel sector, which means that the 2/3 star hotels have to keep innovating in order to remain competitive. This study uses the Service Quality framework to describe customer expectations and identify gaps in hotel services. A questionnaire survey of 105 local tourists who had stayed in 2/3 star hotels in Bandung showed that the most important dimension is responsiveness, following by reliability, assurance, tangibles and empathy. Thus we conclude that local tourists' primary expectations are that hotels will deliver the service they have promised, be responsive to guests' needs and comply with service standards. Furthermore, these findings validate the earlier prediction that comparing 2/3 star hotel with 5/4 start hotel, the customers are having preliminary knowledge on facilities limitation and friendliness. Tourists using 2/3 star hotels tend to be prepared to accept limited facilities and less friendly staff service.

• Journal of the Korea Safety Management & Science
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• v.10 no.4
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• pp.189-198
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• 2008

At present, companies write daily work record or use bar-code in order to collect distribution flow data in real time. However, it needs additional works to check the record or read the bar-code with a scanner. In this case, human error could decrease accuracy of data and it would cause problems in reliability. To solve this problem, RFID (Radio Frequency Identification) is introduced in many automatic recognition sector recently. RFID is a technology that identification data is inserted into micro-mini IC chip and recognize, trace, and manage object, animal, or person using wireless frequency. This is being emerged as the core technology in future ubiquitous environment. This study is intended to suggest RFID application method in franchise business. Traceability and visibility of individual product are supplied based on EPCglobal network. It includes DW system which supplies various assessment data about product in supply chain, financial transaction system which is based on product transaction and position information, and RFID middleware which refines and divides product data from RFID tag. With the suggested application methods, individual product's profile data are supplied in real time and it would boost reliability to customer and make effective cooperation with existing operation systems (SCM, CRM, and e-Business) possible.

• Journal of Biosystems Engineering
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• v.31 no.6 s.119
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• pp.514-523
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• 2006

Production of green pepper has been increased due to customer's preference and a projected ten-year boom in the industry in Korea. This study was carried out to develop an automatic grading and sorting system for green pepper using machine vision. The system consisted of a feeding mechanism, segregation section, an image inspection chamber, image processing section, system control section, grading section, and discharging section. Green peppers were separated and transported using a bowl feeder with a vibrator and a belt conveyor, respectively. Images were taken using color CCD cameras and a color frame grabber. An on-line grading algorithm was developed using Visual C/C++. The green peppers could be graded into four classes by activating air nozzles located at the discharging section. Length and curvature of each green pepper were measured while removing a stem of it. The first derivative of thickness profile was used to remove a stem area of segmented image of the pepper. While pepper is moving at 0.45 m/s, the accuracy of grading sorting for large, medium and small pepper are 86.0%, 81.3% and 90.6% respectively. Sorting performance was 121 kg/hour, and about five times better than manual sorting. The developed system was also economically feasible to grade and sort green peppers showing the cost about 40% lower than that of manual operations.

• Journal of Biosystems Engineering
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• v.26 no.6
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• pp.571-578
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• 2001

Production of green pepper has increased for ten years in Korea, as customer's preference of a pepper tuned to fiesta one. This study was conducted to develop an on-line fading algorithm of green pepper using machine vision and aimed to develop the automatic on-line grading and sorting system. The machine vision system was composed of a professive scan R7B CCD camera, a frame grabber and sets of 3-wave fluorescent lamps. The length and curvature, which were main quality factors of a green pepper were measured while removing the stem region. The first derivative of the thickness profile was used to remove the stem area of the segmented image of the pepper. A new boundary was generated after the stem was removed and a baseline of a pepper which was used for the curvature determination was also generated. The developed algorithm showed that the accuracy of the size measurement was 86.6% and the accuracy of the bent was 91.9%. Processing time spent far grading was around 0.17 sec per pepper.