• Korean Security Journal
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• 제11호
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• pp.159-181
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• 2006

This study is concerned on Why The Certified Security certification is needed and How to control the security quality to get better service to the clients. Theses days are required The Certified Certificate in all the industry. And in this point of view, the certified certificate is a kind of confirmation by an authority to the person who has how much special knowledge and practice in a certain field. Moreover, in the functionalism society the certified certificate system would be very positive effect to the related industry and society as official measurement by an authority. The security is freedom from fear and anxiety. Which means the security can not be operated in isolation from citizen's safe-living expectation, and which is also dealing with valuable human being's life. For getting the better purpose the security industry employees should have more organized special training and education. As my understanding the certified certificate exam system is the confirmation by an authority, the certified certificate is only neutral evidence to get the confidence and credit from the clients. In this point of view the core point is How to control The Certified Certificate by a credied authority.

• Culinary science and hospitality research
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• 제17권1호
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• pp.124-141
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• 2011

The domestic food culture in Korea has gone through great changes, among which dining out is considered most prominent. These changes are caused by a number of factors: development of several related industries, increase in dining out chances, diversification of consumer needs, change in consumption awareness and quality, increase in disposable income, women's advance into the society, etc. Under these circumstances, the fast food industry has been experiencing slow growth, whereas family restaurants and take out coffee franchises are consistently increasing the number of restaurants and shops as a part of aggressive management to increase sales. This study aims, first, to examine the effects of consumer's brand individuality and satisfaction on brand trust, brand emotion, brand identification, and brand loyalty of specialty coffee shops. Subsequently, this study observes how consumer's brand individuality and satisfaction affect the formation of brand trust, brand emotion, brand identification, and brand loyalty in accordance with preferred type of specialty coffee shops, frequency of experience, and life style in order to establish relevant strategies. The results of this study are as follows. First, brand individuality had a positive effect on customer satisfaction. Second, customer satisfaction had a positive effect on brand trust. Third, customer satisfaction had a positive effect on brand loyalty. Fourth, customer satisfaction had a positive effect on brand emotion. Fifth, brand trust had a positive effect on brand loyalty. Sixth, brand emotion had a positive effect on brand loyalty. These results have the following implications. First, the dimensions of brand individuality are presented as a means to form brand loyalty, thereby demonstrating the significance of the effect. Accordingly, developing a proper brand individuality in forming brand loyalty is imperative. Second, consumers using specialty coffee shops had the greatest effect on the relation between dimensions of brand individuality and customer satisfaction in this study, while the interrelation between customer satisfaction and brand loyalty was not concretely supported in the preceding studies.

• Journal of Intelligence and Information Systems
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• 제18권2호
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• pp.177-192
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• 2012

This is a study of match-making systems that considers the mutual satisfaction of matching partners. Recently, recommendation systems have been applied to people recommendation, such as recommending new friends, employees, or dating partners. One of the prominent domain areas is match-making systems that recommend suitable dating partners to customers. A match-making system, however, is different from a product recommender system. First, a match-making system needs to satisfy the recommended partners as well as the customer, whereas a product recommender system only needs to satisfy the customer. Second, match-making systems need to include as many participants in a matching pool as possible for their recommendation results, even with unpopular customers. In other words, recommendations should not be focused only on a limited number of popular people; unpopular people should also be listed on someone else's matching results. In product recommender systems, it is acceptable to recommend the same popular items to many customers, since these items can easily be additionally supplied. However, in match-making systems, there are only a few popular people, and they may become overburdened with too many recommendations. Also, a successful match could cause a customer to drop out of the matching pool. Thus, match-making systems should provide recommendation services equally to all customers without favoring popular customers. The suggested match-making system, called Mutually Beneficial Matching (MBM), considers the reciprocal satisfaction of both the customer and the matched partner and also considers the number of customers who are excluded in the matching. A brief outline of the MBM method is as follows: First, it collects a customer's profile information, his/her preferable dating partner's profile information and the weights that he/she considers important when selecting dating partners. Then, it calculates the preference score of a customer to certain potential dating partners on the basis of the difference between them. The preference score of a certain partner to a customer is also calculated in this way. After that, the mutual preference score is produced by the two preference values calculated in the previous step using the proposed formula in this study. The proposed formula reflects the symmetry of preferences as well as their quantities. Finally, the MBM method recommends the top N partners having high mutual preference scores to a customer. The prototype of the suggested MBM system is implemented by JAVA and applied to an artificial dataset that is based on real survey results from major match-making companies in Korea. The results of the MBM method are compared with those of the other two conventional methods: Preference-Based Matching (PBM), which only considers a customer's preferences, and Arithmetic Mean-Based Matching (AMM), which considers the preferences of both the customer and the partner (although it does not reflect their symmetry in the matching results). We perform the comparisons in terms of criteria such as average preference of the matching partners, average symmetry, and the number of people who are excluded from the matching results by changing the number of recommendations to 5, 10, 15, 20, and 25. The results show that in many cases, the suggested MBM method produces average preferences and symmetries that are significantly higher than those of the PBM and AMM methods. Moreover, in every case, MBM produces a smaller pool of excluded people than those of the PBM method.

• Journal of Intelligence and Information Systems
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• 제16권3호
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• Journal of Nutrition and Health
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• 제55권5호
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• pp.587-600
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• 2022

Purpose: This study was performed to analyze customer needs for the operation of unmanned food stores on a university campus. Methods: A total of 329 responses were collected from customers who had purchased food at the stores. Statistical analyses were conducted using the SPSS program for frequency analysis, χ2-test, t-test, one-way analysis of variance, and Duncan's multiple range test. Results: On average, the overall satisfaction score for lunch and the eating location was 3.91 out of 5 points. Overall satisfaction was significantly higher for home cooking, and lower for eating at convenience stores (p < 0.001), and the score for eating with family was significantly higher than for eating alone or with friends (p < 0.001). On average, the intention to use an unmanned store received a score of 3.98 out of 5 points, while 'home cooking (3.76)' was significantly lower than other eating locations and eating at 'convenience stores (4.38)' was significantly higher than other eating locations (p < 0.05). The desired time of use for unmanned food stores was 'lunch (54.1%)', 'dinner (46.2%)', and 'afternoon snack (41.9%)', and the desired foods were 'doshirak (62.0%)', 'sandwich (56.2%)', 'fruit cup (54.4%)', 'salad (51.7%)', and 'kimbap (50.5%)'. There were significantly more opinions that single-person households would use unmanned food stores for dinner more than family households (p < 0.05), and significantly more willingness to purchase fruit cup (p < 0.05). The average prices (Korean won) that the subjects were willing to pay for the purchase of food were 4,991 for doshirak, 3,403 for salad, 2,745 for fruit cup, 2,609 for sandwich, and 2,235 for kimbap. Conclusion: For the successful operation of the unmanned food stores on the university campus, the store manager should grasp the customer's needs in real-time and establish an effective marketing strategy.

• Journal of Intelligence and Information Systems
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• 제29권3호
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• Journal of Digital Convergence
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• 제12권4호
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• pp.319-326
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• 2014

According to the increasing mobile security users who have experienced authentication failure by forgetting passwords, user names, or a response to a knowledge-based question have preference for biological information such as hand geometry, fingerprints, voice in personal identification and authentication. Therefore biometric verification of personal identification and authentication for mobile security provides assurance to both the customer and the seller in the internet. Our study focuses on human hand biometric information recognition system for personal identification and personal Authentication, including its shape, palm features and the lengths and widths of the fingers taken from mobile phone photographs such as iPhone4 and galaxy s2. Our hand biometric information recognition system consists of six steps processing: image acquisition, preprocessing, removing noises, extracting standard hand feature extraction, individual feature pattern extraction, hand biometric information recognition for personal identification and authentication from input images. The validity of the proposed system from mobile phone image is demonstrated through 93.5% of the sucessful recognition rate for 250 experimental data of hand shape images and palm information images from 50 subjects.

• Culinary science and hospitality research
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• 제21권6호
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• pp.312-330
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• 2015

Domestic coffee franchise market in South Korea has been rapidly growing along with developing the global coffee franchises as well as entering domestic franchise companies into the beverage market. Competitions in the markets are getting more accelerated and local franchises and owner-operated coffee shops are losing their market shares gradually as a result. Local franchises and owner-operated coffee shops in Daegu, however, are notable in that they are maintaining the competitiveness in the markets. This is a unique phenomenon, which deserves to be analyzed to identify the successful attributes as a case study. Important Performance Analysis (IPA) method has been employed for analyzing and its gap(i.e., difference between importance and performance) analysis shows that the price and quantity of coffee are the most important differentiating attributes of local and owner-operated coffee franchises in Daegu.

• Journal of Digital Contents Society
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• 제15권2호
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• pp.227-235
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• 2014

The change from people's work-centered values to their leisure-centered values leads into the change in their life styles. In the circumstance, people's participation into sports activities seems to be an important means to improve their quality of life. Along with the change of the times, more people take part in water leisure sports including kayak. As a result, people's needs for various designs of water leisure goods are on the rise. In this sense, it is necessary to come up with strategies to actively respond to such a change. In this paper, we proposed a user decision-making support system for designing kayaks for leisure. Based on the previous studies and literatures and a questionnaire survey with consumers, it chose the sensitivity related to design. By conducting factor analysis and evaluation, it drew sensitivity and proposed kayak design layouts in the aspect of customer sensitivity preference. It is expected that the result of this study will be used not only for kayak design, but as a design guide for the equipment of water leisure sports, and will be applied for user-friendly design.

• Journal of Digital Convergence
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• 제16권9호
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• pp.497-502
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• 2018

The purpose of this study is to investigate the correlation between female gradation angle and angles of female gradation according to the angle of the treatment. A total of 100 questionnaires were analyzed using questionnaires. The results showed that sophisticated images were observed at $5^{\circ}$, $31^{\circ}$, and $85^{\circ}$ of gradation, and elegance images were observed at $15^{\circ}$ and $65^{\circ}$, and modern images were observed at $50^{\circ}$. Through this study, we will be able to conduct in-depth follow-up research on female gradation cuts classified by the trend position of trends in Korean men, and it will be possible to use it as basic data for deeper haircut education at the same time as creating customer satisfaction.