• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.311-336
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• 2022

This study analyzed the contents of Goodreads review data, which is a social cataloging service with the participation of book users around the world, to identify the preference factors that affect book users' book recommendations in the library information service environment. To understand user preferences from a more detailed point of view, sub-datasets for each rating group, each book, and each user were constructed in the sample selection process. Stratified sampling was also performed based on the result of topic modeling of review text data to include various topics. As a result, a total of 90 preference factors belonging to 7 categories('Content', 'Character', 'Writing', 'Reading', 'Author', 'Story', 'Form') were identified. Also, the general preference factors revealed according to the ratings, as well as the patterns of preference factors revealed in books and users with clear likes and dislikes were identified. The results of this study are expected to contribute to more sophisticated recommendations in future recommendation systems by identifying specific aspects of user preference factors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• 한국노년학
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• v.29 no.3
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• pp.1123-1140
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• 2009

The purpose of this study is to explore the process for the elderly with palsy to solve conflicts with the elderly with dementia in day care center through the narrative therapy group work. For this, the researcher enacted the Narrative therapy group work on the 12 elderly with palsy for seven sessions, and analysed these data through qualitative case study method. The results of this study are as follows. First, the elderly with palsy became to recognize the elderly with dementia as 'the people suffering from dementia' rather than 'the problem', and tried to care them rather than to blame. Second, they became to recognize themselves as 'component people' rather than 'the victim'. Third, they grew intimate and collaborated with themselves. Fourth, they participated actively in day care center. It was discussed connection between these changes and narrative therapy skills, such as to separate problem from people, to respect client as expert for one's problem, to seek unique outcome. The recommendation is suggested that it is important to enlarge choice to participate for long-term care service consumer and to apply narrative therapy on the elderly.

• Archives of Plastic Surgery
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• v.51 no.1
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• pp.139-145
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• 2024

Background Applying into plastic surgery (PS) is competitive. Lacking a home residency program (HRP) is another barrier. Our goal is to characterize challenges faced by PS applicants without HRPs and identify solutions. Methods Surveys were designed for current integrated PS residents and applicants in the 2022 Match without HRPs. Surveys were distributed electronically. Only U.S. allopathic graduate responses were included. Results Of 182 individuals surveyed, 74 responded (39%, 33 residents, 41 applicants). Sixty-six percent reported feeling disadvantaged due to lack of an HRP. Seventy-six percent of applicants successfully matched. Of these, 48% felt they required academic time off (research year) versus 10% of unmatched applicants. Ninety-seven percent of matched applicants identified a mentor versus 40% of unmatched applicants (p < 0.05). Matched applicants identified mentors through research (29%) and cold calling/emailing (25%). Matched versus unmatched applicants utilized the following resources: senior students (74 vs. 10%, p < 0.05) and social media (52 vs. 10%, p < 0.05). Among residents, 16 had PS divisions (48%). Thirty-six percent with divisions felt they had opportunities to explore PS, compared with 12% without divisions. Residents without divisions felt disadvantaged in finding research (94 vs. 65%, p < 0.05), delayed in deciding on PS (50 vs. 28%), and obtaining mentors (44 vs. 35%) and letters of recommendation (31 vs. 24%). Conclusion PS residents and applicants without HRPs reported feeling disadvantaged when matching. The data suggest that access to departments or divisions assists in matching. We identified that external outreach and research were successful strategies to obtain mentorship. To increase awareness for unaffiliated applicants, we should increase networking opportunities during local, regional, and national meetings.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.856-874
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• 2019

The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.

• Journal of Distribution Science
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• v.15 no.5
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• pp.53-64
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• 2017

Purpose - This study focuses on use intention of mobile travel Apps by Chinese tourists visiting Korea based on UTAUT model, ISS model and ITM model. And the corresponding market promotion schemes are proposed for operators of mobile travel Apps by the research results. Research design, data, and methodology - After collecting 326 respondents in China with cross-sectional questionnaires, this study begins the empirical research with users of mobile travel Apps, and analyzes data with IBM SPSS 23.0 and IBM AMOS 23.0. Results - The results of this study include the following aspects: firstly, the System quality and Information quality are accepted for hypotheses of Satisfaction and Performance expectancy. Secondly, the Personal Propensity to Trust and Firm Reputation are accepted for Initial Trust hypothesis, and the hypotheses of Firm Reputation and Initial Trust are accepted for Use Intention. Thirdly, the Performance expectancy, Effort expectancy, Social influence are accepted for Use Intention hypothesis. Conclusions - With the increase of tourists visiting Korea, it can be predicted that the needs visiting Korea will be increased persistently for Chinese - this trend brings about the increase of the Chinese travel. First, information quality greatly influences satisfaction and performance expectancy. The research result shows that, the higher the mobile traveling App's information quality is, the higher the satisfaction and performance expectancy will be. Therefore, operators of mobile traveling App should have in-depth investigations towards users, to know the latter's real demand to the information quality and then provide corresponding services. Second, performance expectancy and effort expectancy greatly influence users' intention. Therefore, mobile traveling App operators should improve Apps' convenience and efficiency and, in doing so, find an effective method for market expansion. Third, social influence greatly affects users' intention. The result shows that mobile traveling App operators should pay attention to the influence of mass media and friends' recommendation on users, thereby it is necessary to improve advertisement activities. Fourth, initial trust also influences users' intention. The result shows that initial trust is a key element inducing users to generate use intention. Therefore, mobile traveling Apps operators should make efforts to catch elements that influence users' initial trust.

• Journal of Distribution Science
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• v.15 no.12
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• pp.73-84
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• 2017

Purpose - Over recent years, O2O and shared economy have been an eye-catching topic. Many researches on O2O and shared economy have been published gradually. The emerging enterprise of chauffeured car services developed rapidly in the past two years. Therefore, it is necessary to explore the influencing factors of use intention of the chauffeured car services users. Through active use of O2O and shared economy, put up with operation strategy in line with their use intention. Research design, data, and methodology - After collecting 324 respondents in China with questionnaires, this study begin the empirical research with users of Chauffeured Car Services, and analyzes data with IBM SPSS 24.0 and IBM AMOS 24.0. Results - Personal Propensity to Trust significantly affects the Initial Trust of chauffeured car services users. Firm Reputation significantly affects the Initial Trust and use intention of chauffeured car services users. Initial Trust significantly affects the use intention of chauffeured car services users. Performance Expectancy and Effort Expectancy significantly affect chauffeured car services users' use intention. Social Influence also significantly affects the use intention of chauffeured car services users. Conclusions - First, Initial Trust significantly affects the use intention of chauffeured car services users. Thus, the enterprise should make efforts to improve users' initial trust in order to attract their attention. For this reason, chauffeured car services enterprises should conduct questionnaires to deeply explore what needs can improve users' initial trust. Second, performance expectancy and effort expectancy significantly affect chauffeured car services users' use intention. When users enjoy chauffeured car services, they attach great importance to the convenience, simplicity and efficiency, which reflects that chauffeured car services' desire for greater development in the O2O and shared economy market. Therefore, they need to grasp users' needs (convenience, simplicity and efficiency) and carefully improve the quality of chauffeured car services. Finally, social influence also significantly affects the use intention of chauffeured car services users. It means friend recommendation or mass media influences users' intention. So, it is more important to increase differentiated benefits, advertising and publicity of chauffeured car services.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.309-314
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• 2017

In general, there are many difficulties in collecting, storing, and analyzing SNS (social network service) data, since those data have big data characteristics, which occurs very fast with the mixture form of structured and unstructured data. In this paper, we propose a new data visualization framework that works on Apache Storm, and it can be useful for real-time and dynamic analysis of SNS data. Apache Storm is a representative big data software platform that processes and analyzes real-time streaming data in the distributed environment. Using Storm, in this paper we collect and aggregate the real-time Twitter data and dynamically visualize the aggregated results through the tag cloud. In addition to Storm-based collection and aggregation functionalities, we also design and implement a Web interface that a user gives his/her interesting keywords and confirms the visualization result of tag cloud related to the given keywords. We finally empirically show that this study makes users be able to intuitively figure out the change of the interested subject on SNS data and the visualized results be applied to many other services such as thematic trend analysis, product recommendation, and customer needs identification.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.109-128
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• 2010

The purpose of our research is to figure out the 'non-financial value' of consumers applying networks amongst consumer groups, the data-based marketing strategy to the analysis and delve into the ways for enhancing effectives in marketing activities by adapting the value to the marketing. To verify the authenticity of the points, we did the empirical test on the consumer group using 'the Essence Cosmetics Products' of high involvement that is deeply affected by consumer perceptions and the word-of-mouth activities. 1) The empirical analysis reveals the following features. First, the segmented market for 'Essence Consumer' is composed of several independent networks, each network shows to have the consumers that is high degree centrality and closeness centrality. Second, the result proves the authenticity of the non-financial value for boosting corporate profits by the high degree centrality and closeness centrality consumer's word-of-mouth activities. Lastly, we verify that there lies a difference in the network structure of 'Essence Cosmetics Market'per each product origin(domestic, foreign) and demographic characteristics. It does, therefore, indicate the need to consider the features applying mutually complementary for the network targeting.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.145-147
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• 2004

본 논문에서는 무선인터넷 환경에 적합한 개인화된 상품추천에이전트를 제안한다. 기존에 유선인터넷상의 많은 개인화 추천시스템에서는 초기 사용자 모델링을 위해 사용자에게 수많은 질의를 하고 응답을 요구하였다. 그러나 이러한 방식은 무선인터넷 환경에서 정보 전송량에 따른 높은 사용요금을 고려할 때 적용하기 힘든 방식이다. 본 제안 시스템은 사용자의 Social data률 이용하여 사용자를 비슷한 연령과 성별 그룹으로 나누고, 해당 그룹에서 구매율이 높은 상품을 우선 제시한 후, 사용자 행동을 모니터링 하여 암시적(Implicit)피드백을 통해 프로파일을 생성함으로써, 번거로운 질의-응답 과정 없이도 초기 사용자 모델링을 수행할 수 있다. 프로파일 생성 이후에는 이를 기반으로 하여 사용자몰 유사한 취향을 가진 그룹으로 다시 군집화한 후 협력적 추천을 하게 되며, 프로파일에는 해당 상품의 최종 카테고리명과 키워드를 수집함으로써, 상품의 브랜드와 규격정보를 반영한 추천이 가능하다. 또한 추천 상품과 사용자의 구매데이터와의 비교를 수행하여 사용자가 해당상품을 구매하였을 경우, 상품에 대한 취향정보는 그대로 유지하고 관련 상품을 추천하되, 구매한 상품이 중복 추천되지 않도록 하였다. 시스템 평가를 위해 프로토타입을 구현하여, 다수의 사용자에게 시스템을 이용하며 관심품목을 체크하도록 하였고. 추천횟수가 반복되며 히트율이 증가하는 결과를 통해 시스템의 학습속도와 성능을 평가하였다. 그리고 쇼핌몰에서 구매경험이 있는 사용자의 기존 구매데이터와 Social data를 이용한 초기 제시상품을 역으로 비교하여 오랜 시간과 비용 발생 없이도 초기 프로파일 생성의 유효성을 증명하였다. 포함하는 XML 질의에 대해서도 웹에서 캐쉬를 이용한 처리가 효율적임을 확인하였다.키는데 목적이 있다.RED에 비해 향상된 성능을 보여주었다.웍스 네트워크상의 다양한 디바이스들간의 네트워크 다양화와 분산화 기능을 얻을 수 있었고, 기존의 고가의 해외 솔루션인 Echelon사의 LonMaker 소프트웨어를 사용하지 않고도 국내의 순수 솔루션인 리눅스 기반의 LonWare 3.0 다중 바인딩 기능을 통해 저 비용으로 홈 네트워크 구성 관리 서버 시스템 개발에 대한 비용을 줄일 수 있다. 기대된다.e 함량이 대체로 높게 나타났다. 점미가 수가용성분에서 goucose대비 용출함량이 고르게 나타나는 경향을 보였고 흑미는 알칼리가용분에서 glucose가 상당량(0.68%) 포함되고 있음을 보여주었고 arabinose(0.68%), xylose(0.05%)도 다른 종류에 비해서 다량 함유한 것으로 나타났다. 흑미는 총식이섬유 함량이 높고 pectic substances, hemicellulose, uronic acid 함량이 높아서 콜레스테롤 저하 등의 효과가 기대되며 고섬유식품으로서 조리 특성 연구가 필요한 것으로 사료된다.리하였다. 얻어진 소견(所見)은 다음과 같았다. 1. 모년령(母年齡), 임신회수(姙娠回數), 임신기간(姙娠其間), 출산시체중등(出産時體重等)의 제요인(諸要因)은 주산기사망(周産基死亡)에 대(對)하여 통계적(統計的)으로 유의(有意)한 영향을 미치고 있어 $25{\sim}29$세(歲)의 연령군에서, 2번째 임신과 2번째의 출산에서 그리고 만삭의 임신 기간에, 출산시체중(出産時體重) $3.50{\sim}3.99kg$사이의 아