• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• Korean Journal of Cleft Lip And Palate
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• v.4 no.2
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• pp.1-8
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• 2001

The dissimilarities between South and North Korea have persisted in spite of the reconciliation campaign by both countries. The situation of the cleft lip & palate of North Korea was very unclear until now. The purpose of this study is to understand all the current facts of cleft lip & palate in North Korea so that we can find ways of helping North Korea in this field of medicine. The present data and analysis are extracted from North Korean textbooks. The results are as follow. 1. In North Korea, patients with CLP are treated by oral surgeons or maxillofacial surgeons. The detailed contents about the CLP are well described in the North Korean textbooks for the dental students. 2. The terminology of CLP in North Korea has changed from time to time, but the present terminology not being so different from South Korean counterpart. So there will be no particular problems in mutual communication. 3. The main classification for CLP in North Korea originated from Kernahan & Stark's classification as is with South Korea. 4. The incidence of CLP is 1 : 1,000-1,200 in North Korea, which is lower than that of South Korea. There is, however, some difference between the North and South Korean CLP in detailed statistics. 5. We found the North Korean physicians have shown much interest in pursuing the etiology and the prevention of CLP. 6. The timing of CLP operations varied a lot in North Korea. There was recommendation by few for the operation in much late age than in South Korea. 7. The classical operation techniques of cleft lip have changed. For unilateral cleft lip Tennison-Randall method was replaced by Millard I method: and for bilateral cleft lip LeMesurier method was replaced by Veau III and Tennison methods. But for cleft palate Pushback palatoplasty has been utilized consistently.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.389-390
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• 2002

It is important that acquire information about if customer has some habit in electronic commerce application of internet base that led in recommendation service for customer in dynamic web contents supply. Collaborative filtering that has been used as a standard approach to Web personalization can not get rapidly user's preference change due to static user profiles and has shortcomings such as reliance on user ratings, lack of scalability, and poor performance in the high-dimensional data. In order to overcome this drawbacks, Web usage mining has been prevalent. Web usage mining is a technique that discovers patterns from We usage data logged to server. Specially. a technique that discovers Web usage patterns and clusters patterns is used. However, the discovery of patterns using Afriori algorithm creates many useless patterns. In this paper, the enhanced method for the construction of dynamic user profiles using validated Web usage patterns is proposed. First, to discover patterns Apriori is used and in order to create clusters for user profiles, ARHP algorithm is chosen. Before creating clusters using discovered patterns, validation that removes useless patterns by Dempster-Shafer theory is performed. And user profiles are created dynamically based on current user sessions for Web personalization.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.52-61
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• 2012

Text visualization is one of sectors in data visualization. This study is on methods to visually represent text's contents, structure, and form aspects based on various analytic techniques about wide range of text data. In this study -as a text visualization study-, 1) a method to find out the characteristics of a book's genre using words in the text of the book was looked into, 2) elements of visualization of a book's genre based on verification through an experiment were drew, and 3) the ways to intuitionally and efficiently visualize this were explained. According to visualization suggested by this study, first, actual genre of a book can be understood based on words used in the book. Second, with which genre is closed to the book can be found out with one glance through images of visualization. Moreover, the characteristics of complicated genres included in a book can be understood. Furthermore, the level of closeness (similarity) of a genre -which is found to be a representative genre using the number of dots, curvature of a curve, and brightness in the image- can be assumed. Finally, the outcome of this study can be used for a variety of fields including book customizing service such as a book recommendation system that provides images of personal preference books or genres through application of books favored by individual customers.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.713-728
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• 2022

Purpose: This study examined the effect of VR safety education content quality on behavioral intention and collect operational opinions through interview. Method: Based on the survey data of 93 former and current officers, the hypothesis was verified. In addition, 15 fire safety experience centers were visited to conduct interview. Result: For the quality of VR safety education contents, immersion and convenience had a significant effect on usage satisfaction, recommendation intention, and field application intention. In addition, convenience and aesthetic experience had a significant effect on the educational effect, but immersion and diversity did not significant. In the interview, they suggested that VR education has high user satisfaction and good educational effects. The quality of content(particularly immersion and convenience) is an important factor in VR education. In the long-term persepective, it is necessary to prepare a standard teaching plan for each disaster, in addition, manpower, expertise, maintenance problems, and etc. Conclusion: Through these results, it was confirmed that VR experience content quality affects behavioral intention and educational effect and that efforts and investments to improve content quality are needed to enhance the effectiveness of VR experience education. And the contents derived from the interview will be helpful in the operation of an effective fire safety experience center.

• Quality Improvement in Health Care
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• v.4 no.1
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• pp.36-49
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• 1997

Background : Hypertension is one of the most important risk factors of the cerebrovascular accident and coronary artery disease which are the major causes of mortality in Korea. In Korea, the quality of care provided by office-based physicians has not been evaluated formally. The purpose of this study is to assess the quality of hypertension management of office-based physicians. Method : Self-administered questionnaires were mailed to the office-based physicians with the speciality of internal medicine, general surgery, family medicine, and general practitioners. Among 2,045 physicians, 981 doctors(48.0%) replied the questionnaires. Contents of questionnaires were based on the recommendation from the JNC-V report(the Fifth Report of the Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure), and included the criteria of diagnosis, treatment, follow-up interval, and other characteristics of physicians(age, sex, type of speciality, and location of practice). Results : Eighty four percent of the office-based physicians made diagnosis of hypertension with less than 3 times of blood pressure measurements. The performance rate of required examination for hypertensives was very low in most items. Rate of fundoscopic examination is the lowest one among them(5.9%). The performance rate of laboratory examination was also low in most items. Internists tended to order more frequent laboratory examinations than any other type of physicians. Only 11.4% of the physicians did appropriate treatments for the mild hypertension case. The antihypertensives selected by the physicians as a first line drug were in the order of beta blocker(26.4%), calcium channel blocker(23.4%), diuretics(23.1%), ACE inhibitors(14.3%). The visit interval for established hypertensives was very short. Proportion of physicians with follow-up interval longer than 4 weeks was only 4.3%. Conclusions : The overall quality of hypertension management of office-based physicians in Korea is very problematic in many aspects. So further investigations to find out the reasons of low quality arid quality of care should be initiated.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.1
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• pp.100-108
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• 2011

The object of this study was two. One was if Polygonati Rhizoma preparata had a benzo(a)pyrene, the other was to evaluate the single dose toxicity of 9 repetitive steaming and fermenting processed Polygonati Rhizoma, dried root parts of Polygonati Rhozoma preparata extract, in male and female mice. We measured a content of benzo(a)pyrene in Polygonati Rhozoma preparata using a method with HPLC/FLD. And for single dose toxicity, aqueous extracts of Polygonati Rhozoma preparata (EPP; Yield = 35.4 %) was administered to female and male ICR mice as an oral dose of 2,000, 1,000 and 500 mg/kg (body weight) according to the recommendation of Korea Food and Drug Administration (KFDA) Guidelines. Animals were monitored for the mortality and changes in body weight, clinical signs and gross observation during 14 days after dosing, upon necropsy; organ weight and histopathology of 12 principle organs were examined. As results, we could not find any mortality, clinical signs, and changes in the body and organ weight except for slight soft feces sporadically detected in EPP treated male mice at 1 day after administration. In addition, no EPP-treatment related abnormal gross findings and changes in histopathology of principle organs were detected except for some sporadic accidental findings. The results obtained in this study suggest that benzo(a)pyrene was not existed in Polygonati Rhozoma preparata and the 50% lethal dose and approximate lethal dose of EPP aqueous extracts in both female and male mice were considered as over 2,000 mg/kg, the limited highest dosage recommended by KFDA Guidelines. However, it also observed that the possibilities of digestive disorders, like soft feces when administered over 500 mg/kg of EPP aqueous extracts in the present study.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.616-621
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• 2006

Recently many cars are equipping a navigation system. The main purpose of the early system guides a user through the route. A navigation system includes various abilities by development of various technologies and it has given more convenience to user. It can play various records on the tape and announces which are useful information about each road. Also it can use various multi-media contents by DMB device during driving. However, guide function of basic and important road in the navigation system has not grown greatly yet. In this paper, we proposed recommendation method of human-friendly road considering user's condition through various information of outside environment, user's velocity intention, a driver's emotion and a preference of the road. Modules consists of hierarchical structure that can easily correct and add each algorithm and those use fuzzy logic algorithm.