• Asia Marketing Journal
• /
• v.9 no.3
• /
• pp.117-149
• /
• 2007

It can be explained by congruity theory as a process that consumers engage in a matching process to identify personal community that is congruent with their self-images to find the identification between the self and the personal community. Personal community cues that evoke certain images are viewed as activating similar beliefs about the self (e.g., high status). Individuals prompt a comparison process to determine whether the personal community and self-image are congruent and imagine prototypical users of the personal community and select ones that maximize similarity to their actual or desired self-concept. Identity is devided into personal identity and social identity. Consumers are likely to be influenced by both personal identity and social identity. In this article the influencing factors of the commitment to on-line personal community are explored by the sources of both personal identification and social identification. The results are as follows. The maintenance expression and enhancement expression of personal self influence the level of personal identification positively and the maintenance expression and enhancement expression of social self influence the level of social identification positively. The level of both social and personal identification positively influence the commitment to on-line personal community which gives positive responses to the source enterprise that allows the cyberspace and the other benefits to be used.

• Journal of the Korean Society of Costume
• /
• v.56 no.7 s.106
• /
• pp.54-68
• /
• 2006

This study examined similarity between white porcelain models and dress silhouette of women painted in genre painting in late Joseon period, and found that gourd bottles that are one of main porcelain styles are very similar with dress silhouette of women presented in genre painting in Joseon period; that is, bottle necks of gourd bottles and women's waist, voluminous bottles and erotically full hips, the length of skirts which showed underwear dress and height of porcelain's bottom, waist panel winding slim waist and wide edge of porcelain's mouth. As images are personal mental process of potters who were not socially controlled, they used dress as an indirect means of women's bodies for the subjects of images that are obtained through direct and indirect experiences of the persons who perceive. It indicates that they intended to make their ceramic works express women's dressing style through images of porcelain's models. Such images were sensual, but not superficial. It is suggested that they were influenced by the society which considered ethical experimental mind and practicality of the governing class of that time who enjoyed it metaphorically, but not impudent.

• Korean Journal of Human Ecology
• /
• v.15 no.3
• /
• pp.405-416
• /
• 2006

We, in this thesis, examined the similarities and differences of bat patterns in China and Korea, along with a consideration of how two peoples understood bats and how their understanding of bats developed and also influenced the bat patterns. As for research period, the Ming(明) dynasty and the Qing(淸) dynasty in China and the Chosun dynasty in Korea were considered. The subjects of this study included clothes, personal ornaments, and embroidery. This research was done by comparing and analyzing the similarities and differences of two countries' bat patterns. The results of comparing and analyzing Chinese and Korean bat patterns were as follows: In terms of similarity, people of two countries understood bat patterns as the symbol of happiness. Therefore, they were used in clothes, personal ornaments, and daily necessities. In terms of difference, bat patterns of two countries had different shapes. In China, colorful and realistic single bat patterns were shown as intensive composition, whereas in Korea plain and simple bat patterns were used. These differences resulted from different way of understanding of bat patterns. It can be concluded that the bat patterns can be a great cultural commodity with high commercial values, if the traditional pattern is used as a unique element of the Oriental designs and altered and developed to attract consumers' attention.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.3
• /
• pp.91-101
• /
• 2011

Internet is becoming increasingly popular due to the rapid development of information and communication technology. There has been a convenient social activities such as the mutual exchange of information, e-commerce, internet banking, etc. through cyberspace on a computer. However, by using the convenience of the internet, the personal IDs(identity card, driving license, passport, student ID, etc.) represented by the electronic media are exposed on the internet frequently. Therefore, this study propose a feature extraction method to analyze the characteristics of image files containing personal information and a image retrieval method to find the images using the extracted features. The proposed method selects the feature information from color, texture, and shape of the images, and the images as searched by similarity analysis between feature information. The result which it experiments from the image which it acquires from the web-based image DB and correct image retrieval rate is 89%, the computing time per frame is 0.17 seconds. The proposed method can be efficiently apply a system to search the image files containing personal information and to determine the criteria of exposure of personal information.

• Journal of Digital Convergence
• /
• v.15 no.4
• /
• pp.191-198
• /
• 2017

It is needed to support intelligent customized health information service for user convenience in PHR based Personal Health Care Service Platform. In this paper, we specify an ontology-based health data model for Personal Health Care Service Platform. We also design a knowledge search algorithm that can be used to figure out similar health record by applying machine learning and data mining techniques. Axis-based mining algorithm, which we proposed, can be performed based on axis-attributes in order to improve relevance of knowledge exploration and to provide efficient search time by reducing the size of candidate item set. And K-Nearest Neighbor algorithm is used to perform to do grouping users byaccording to the similarity of the user profile. These algorithms improves the efficiency of customized information exploration according to the user 's disease and health condition. It can be useful to apply the proposed algorithm to a process of inference in the Personal Health Care Service Platform and makes it possible to recommend customized health information to the user. It is useful for people to manage smart health care in aging society.

• Journal of Information Technology and Architecture
• /
• v.9 no.3
• /
• pp.333-344
• /
• 2012

This paper proposes a system which recommends movies using information from social network services containing personal interest and taste. Method for establishing data is as follows. The system gathers movies' information from web sites and user's information from social network services such as Facebook and twitter. The data from social network services is categorized into six steps of emotion level for more accurate processing following users' emotional states. Gathered data will be established into vector space model which is ideal for analyzing and deducing the information with the system which is suggested in this paper. The existing similarity measurement method for movie recommendation is presentation of vector information about emotion level and similarity measuring method on the coordinates using Cosine measure. The deducing method suggested in this paper is two-phase arithmetic operation as follows. First, using general cosine measurement, the system establishes movies list. Second, using similarity measurement, system decides recommendable movie list by vector operation from the coordinates. After Comparative Experimental Study on the previous recommendation systems and new one, it turned out the new system from this study is more helpful than existing systems.

• Fashion & Textile Research Journal
• /
• v.21 no.2
• /
• pp.141-150
• /
• 2019

Online recommendation services help people search for an appropriate product among a huge assortment in stores that also minimize consumers' choice overload. People with a need for uniqueness are likely to prefer this online recommendation service based on individual needs and tastes. This study verifies the effect of consumers' choice overload and similarity avoidance in consumers' evaluation towards an online recommendation service with a focus on innovativeness and use comparability. Two-hundred consumers participated in this study and data were collected through an online survey firm. A mock retailer's webpage was created and showed six types of sneakers, which was presented as a result of product recommendation based on consumers' personal information. Data was analyzed using confirmatory factor analysis (CFA), analysis of variance (ANOVA), and regression analysis. The results show that people with a high similarity avoidance perceive an online recommendation service as an innovative and compatible service. They also perceive a high level of use compatibility for an online recommendation service, especially when it is difficult to choose a product under choice overload. Innovativeness and use compatibility of an online recommendation service increase behavioral intention. The results of this study can contribute to strategies to start online recommendation services from online retailers' websites that identify circumstances in which consumers can adopt innovative services in a positive manner.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.1-17
• /
• 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 the Institute of Electronics and Information Engineers
• /
• v.51 no.3
• /
• pp.147-155
• /
• 2014

While variety of content on the internet has increased with the development of IT and person's needs about suitable information are increasing rapidly, studies for personalized service have been actively performed. In the study, we proposed the Hypertension and Diabetes risk analysis system according to personal food intake preference using the analysis method of buying preferences in product recommendation system. For the analysis of food intake preference, the Pearson correlation coefficient is used to calculate similarity weights between each reference analysis data and sample data and then reference data should be grouping into the similarity weights and calculating risk of hypertension and diabetes each group. To evaluate the significance of this system, 1,021 subjects are applied the system. Hypertension and diabetes groups' risk is significant higher than normal group statistically so, it is confirmed that food intake preference and the diseases were relevant. In this paper, we verify the validity of hypertension and diabetes risk analysis system using a personal food intake preference.

• Journal of the Korea Computer Industry Society
• /
• v.3 no.9
• /
• pp.1269-1278
• /
• 2002

The more high growth of knowledge, the more need personal identity technique. Fingerprint or iris of the eye identity techniques are already commercialized and used various field. Using human face recognition or authentication are not high performance yet. But application for an organism or face recognition are expected getting important. We propose a user recognition system by verifying similarity comparison of eye and lip component images which are splitted, calculated characteristic rate of each facial components and added weight to special formula. Through test proposed methods and analysis the result, we got a high recognition rate.