• Journal of Korean Dental Science
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• v.6 no.2
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• pp.78-86
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• 2013

Purpose: The purpose of this study was to assess the effects of lip anteroposterior position based on esthetic line on the perceived attractiveness. Materials and Methods: We selected a 20s female standard lateral photograph which was within average range of cephalometric analysis, modified lips anteroposterior position based on esthetic line into 5 pictures. This study investigated and compared the preference of facial profile among the groups; male : female and dental relevance: non-dental relevance. Total 255 judges (male : female=138 : 117, relevant : non-relevant=159 : 96) who were 20s to 30s were asked to rate these photographs based in lip attractiveness using visual analogue scale (VAS). Result: All groups had similarity the average of VAS of moved backward lips 2 mm were highest and moved forward lips 4 mm were lowest. Comparing between male group and female group, there were significant differences in all pictures except for original which was not modified. In the dental groups, moved forward lips 2 mm had significant difference and the average in dental relevant group were lower than non-relevant group in lip protrusion. Conclusion: The preference about lip protrusion was similar irrespective of dental knowledge or gender. All groups preferred retrusion of lips to protrusion of lips. In female group, they had higher the average of VAS. In relevant group, they disliked protrusion rather than retrusion of lips significantly.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.133-140
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• 2012

In this paper, we propose a music recommendation method considering users' tags by collaborative tagging in a social music site. Since collaborative tagging allows a user to add keywords chosen by himself to web resources, it provides users' preference about the web resources concretely. In particular, emotional tags which represent human's emotion contain users' musical preference more directly than factual tags which represent facts such as musical genre and artists. Therefore, to classify the tags into the emotional tags and the factual tags and to assign weighted values to the emotional tags, a tag ontology called UniTag is developed. After preprocessing the tags, the weighted tags are used to create user profiles, and the music recommendation algorithm is executed based on the profiles. To evaluate the proposed method, a conventional playcount-based recommendation, an unweighted tag-based recommendation, and an weighted tag-based recommendation are executed. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.

• Fashion & Textile Research Journal
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• v.16 no.2
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• pp.245-254
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• 2014

The purpose of this study was to describe 20's to 30's men's fashion lifestyle and develop clusters in grooming related variables. We also tried to interpret profile of clusters, and determine the difference between different level of grooming clusters in appearance care behaviors, and clothing behaviors such as clothing selection, preference of clothing image and design in men's wear. Data was obtained using the survey methods by convenience sampling. Frequency analysis, factor analysis, cluster analysis, chi-square test, and t-test were used for analysis using SPSS 18.0. The result of factor analysis of men's lifestyle show that 5 factors are extracted. Two different clusters were formed after the K-means cluster analysis. We realized that the level of grooming activity is significantly associated with the young men's major expenditure item, and beauty/care items, and the reason for exercise. The level of grooming was strongly associated with clothing selection behaviors. In addition, there is a significant difference in preferred image between two different grooming groups. In the feminine image, HG group favored more than LG group. The preferred design was associated with the degree of grooming as well. Unique and stylish top and bottom styles such as cargo, hiphop, and boots cut were favored more by HG group than LG group. We suggest that we can do market segmentation by the degree of the grooming activity, considering the current men's taste and trend to extend market share.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.684-695
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• 2001

Recent recommender system uses a method of combining collaborative filtering system and content based filtering system in order to solve sparsity and first rater problem in collaborative filtering system. Collaborative filtering systems use a database about user preferences to predict additional topics. Content based filtering systems provide recommendations by matching user interests with topic attributes. In this paper, we describe a method for discovery of user preference through combining two techniques for recommendation that allows the application of machine learning algorithm. The proposed collaborative filtering method clusters user using genetic algorithm based on items categorized by Naive Bayes classifier and the content based filtering method builds user profile through extracting user interest using relevance feedback. We evaluate our method on a large database of user ratings for web document and it significantly outperforms previously proposed methods.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.358-366
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• 2022

Recently, 'Apartment community facilities' have emerged as the most optional factor in the apartment market, and their level is becoming very important. Therefore, this study derived each type through latent profile analysis centering on health-related community facilities in 126 domestic main apartment complexes, and as a result of the analysis, it was confirmed that it was divided into a Pilates group, GX and Yoga group, Golf and Table Tennis practice range group, and overall low group. Among the four groups, Pilates, GX, and yoga groups are more likely to belong to Gangnam, Seocho and Songpa compared to complexes with many golf and table tennis practice ranges, and at the same time, the transaction price is also the highest. Through these analysis results, it was suggested that changes in the preference for leisure activities should be reflected when constructing community facilities, and that health-related community facilities should be deeply considered in residential welfare policies in consideration of high preference for fitness facilities in youth housing.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.145-162
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• 2008

One of the big concerns in e-society is privacy issue. In special, in developing robust ubiquitous smart space and corresponding services, user profile and preference are collected by the service providers. Privacy issue would be more critical in context-aware services simply because most of the context data themselves are private information: user's current location, current schedule, friends nearby and even her/his health data. To realize the potential of ubiquitous smart space, the systems embedded in the space should corporate personal privacy preferences. When the users invoke a set of services, they are asked to allow the service providers or smart space to make use of personal information which is related to privacy concerns. For this reason, the users unhappily provide the personal information or even deny to get served. On the other side, service provider needs personal information as rich as possible with minimal personal information to discern royal and trustworthy customers and those who are not. It would be desirable to enlarge the allowable personal information complying with the service provider's request, whereas minimizing service provider's requiring personal information which is not allowed to be submitted and user's submitting information which is of no value to the service provider. In special, if any personal information required by the service provider is not allowed, service will not be provided to the user. P3P (Platform for Privacy Preferences) has been regarded as one of the promising alternatives to preserve the personal information in the course of electronic transactions. However, P3P mainly focuses on preserving the buyers' personal information. From time to time, the service provider's business data should be protected from the unintended usage from the buyers. Moreover, even though the user's privacy preference could depend on the context happened to the user, legacy P3P does not handle the contextual change of privacy preferences. Hence, the purpose of this paper is to propose a mutual P3P-based negotiation mechanism. To do so, service provider's privacy concern is considered as well as the users'. User's privacy policy on the service provider's information also should be informed to the service providers before the service begins. Second, privacy policy is contextually designed according to the user's current context because the nomadic user's privacy concern structure may be altered contextually. Hence, the methodology includes mutual privacy policy and personalization. Overall framework of the mechanism and new code of ethics is described in section 2. Pervasive platform for mutual P3P considers user type and context field, which involves current activity, location, social context, objects nearby and physical environments. Our mutual P3P includes the privacy preference not only for the buyers but also the sellers, that is, service providers. Negotiation methodology for mutual P3P is proposed in section 3. Based on the fact that privacy concern occurs when there are needs for information access and at the same time those for information hiding. Our mechanism was implemented based on an actual shopping mall to increase the feasibility of the idea proposed in this paper. A shopping service is assumed as a context-aware service, and data groups for the service are enumerated. The privacy policy for each data group is represented as APPEL format. To examine the performance of the example service, in section 4, simulation approach is adopted in this paper. For the simulation, five data elements are considered: $\cdot$ UserID $\cdot$ User preference $\cdot$ Phone number $\cdot$ Home address $\cdot$ Product information $\cdot$ Service profile. For the negotiation, reputation is selected as a strategic value. Then the following cases are compared: $\cdot$ Legacy P3P is considered $\cdot$ Mutual P3P is considered without strategic value $\cdot$ Mutual P3P is considered with strategic value. The simulation results show that mutual P3P outperforms legacy P3P. Moreover, we could conclude that when mutual P3P is considered with strategic value, performance was better than that of mutual P3P is considered without strategic value in terms of service safety.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.49-58
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• 2011

Recently, GIS(Geographic Information System) has been developed to personalized service for providing the specialized services that is aimed to personal user based on mobile communication. The existing GIS system provides comprehensive and simple information but GIS System for personalized service must provide the adjustive information through the personal interest profile based on POI(PoInt of Interest). This paper describes the intelligent retrieval geographical information service module for providing personal oriented geographic information service. Our proposal model consists of user preference profile, acquisition of POI through hybrid network (Wireless LAN, CDMA), service platform and implementation of prototype system. Implementation model can apply to the life information service like restaurant, oil station, convenient store and etc.

• Journal of the East Asian Society of Dietary Life
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• v.14 no.1
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• pp.39-46
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• 2004

Waxy maize starches are inherently stable in soluble status and can be chemically modified to improve stability along with heat, acid and shear resistance. This study was carried out to investigate the effect of theological and sensory characteristics of the yellow layer cake made by adding different levels of waxy maize starch as a fat substitute for shortening. By increasing the substitution level of waxy maize starch for shortening, the specific gravity of cake batter increased and the viscosity decreased. The microstructures of cake crumb observed by the scanning electron microscope were not different significantly, and the size of air cells and fat particles also were not substantially decreased by increasing fat substitution level. The texture profile analysis using texture analyzer decreased by increasing the different substituting levels of waxy maize starch. Among various sensory properties, the color value of layer cake increased by increasing the level of waxy maize starch. However, the appearance, flavor, taste, texture and overall preference decreased.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.25-34
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• 2017

With the recent spread of smartphones and the introduction of web services, online users can access large-scale content regardless of time or place. However, users have had trouble finding the content they wanted among large-scale content. To solve this problem, user modeling and content recommendation system have been actively studied in various fields. However, in spite of active changes in senior groups according to the changes in information environment, research on user modeling and content recommendation system focused on senior groups are insufficient. In this paper, we propose a method of modeling smart senior based on their preference, and further develop a smart senior classification model using machine learning methods. As a result, we can not only grasp the preferences of smart seniors, but also develop a smart senior classification model, which is the foundation for the research of a recommendation system which will provide the activities and contents most suitable for senior groups.