• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.4
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• pp.93-108
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• 2012

The purpose of this study is to find any contradictions which arise with the use of Wiki in the classroom, and to address such contradictions in order to promote learning among LIS students. This study employed a multi-methodology, including Wiki usage analysis, and interviews with 12 students who participated in an LIS class. Observations revealed that group projects are common in academic classes. Interviewees agreed that their performance through collaborative efforts was higher than that through individually performed activities. However, there were no pre-experiences with Wiki in learning and task-oriented cooperation which gave rise to a controversy. In addition, even though a new technology, as a more advanced form, was suggested, students cooperated with their peers according to their tradition without using the recommended new technology. Therefore, students should be taught about Wiki usage and experience the effective learning which is available to them through collaboration with their peers. Additionally, LIS curriculum should incorporate relationship-oriented activities using Web 2.0 applications with the expectation of enhanced learning among students.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.546-559
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• 2021

This research aimed to develop the district unit plan simulation using procedural modeling based on shape grammar. For this, Esri's CityEngine 2020.0 was selected as a main development tool, and Inside Commercial Area in Bangi-dong, Songpa-gu, Seoul as the research site where about 25% of the total area was developed over the past five years. Specifically, the research developed the simulation through the following three phases of Data-Information-Knowledge after selecting necessary parameters. In the Data phase, 2 and 3 dimensional data were obtained by utilizing data sharing platforms. In the next Information phase, the acquired data were generated into various procedural models according to the shape grammar, and the 2D and 3D layers were then integrated using relevant applications. In the final Knowledge phase, three-dimensional spatial analysis and storytelling contents were produced based on the integrated layer. As a result, the research suggests the following three implications for the simulation development. First, data accuracy and improvement of sharing platforms are needed in order to effectively carry out the simulation development. Second, the guidelines for district unit plans could be utilized and developed into shape grammar for procedural modeling. Third, procedural modeling is expected to be used as an alternative tool for communication and information delivery.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1737-1740
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• 2003

최근 몇 년간 웹 애플리케이션의 개발은 다른 애플리케이션의 개발보다 사회적으로 더욱 활발하였다. 이것은 우리 사회에 많이 보급되어 손쉽게 접근할 수 있는 인터넷과 또 그 인터넷 안에 있는 웹의 편리함에 그 이유가 있을 것이다. 그러나 이러한 활발한 웹 애플리케이션 개발에 비하여 웹 애플리케이션의 개발 방법 자체에 대한 연구는 거의 전무한 상태이다. 따라서 대학 교육에서도 그 일정한 지침이 없는 상태이다. 이러한 개발 방법에 대한 미진한 연구는 아마도 다른 애플리케이션에 비하여 웹 애플리케이션의 개발이 시작된 지 오래되지 않은 원인도 있겠지만 또 웹 애플리케이션이 가지는 특성에도 기인할 것이다. 즉, 웹은 보통 MVC(Model-View-Controller) 모델의 뷰(view)가 중심이 되고 컨트롤 부분이나 모델 부분은 상대적으로 적기 때문이다. 그래서 그 구현이 다른 일반 애플리케이션의 구현보다 비교적 개념이 쉽고 간단하다. 그러나 그 구현이 비교적 간단하다 하더라도 정형화된 개발 모델이 있으면 애플리케이션의 생산성은 향상될 뿐만이 아니라 학교 교육에도 큰 도움이 될 것이다. 즉 학생들에게 정형화 된 모델을 교육하는 경우 그것은 학생들의 웹 애플리케이션 개발에 큰 도움이 되는 것이다. 실제로 개발 모델을 교육시키고 프로젝트를 하는 경우와 그렇지 않은 경우에 그 완성도에서 크나큰 차이를 보였다. 정형화된 모델을 교육받은 학생들은 그렇지 않은 학생들에 비하여 프로젝트의 완성도도 높을 뿐만 아니라 그 구성이나 일관성에서 훨씬 좋은 결과를 보였다. 본 논문에서는 이러한 웹 애플리케이션을 위한 개발 방법을 제시하고 그에 앞서 클라이언트-서버 모델이 웹이라는 환경에서 가질 수 있는 특성과 작업에 대하여 분석한다. 그리고 본 논문에서는 그 예를 JSP로 보여준다.라도 단순 다중 쓰레드 모델보다 더 많은 수의 클라이언트를 수용할 수 있는 장점이 있다. 이러한 결과를 바탕으로 본 연구팀에서 수행중인 MoIM-Messge서버의 네트워크 모듈로 다중 쓰레드 소켓폴링 모델을 적용하였다.n rate compared with conventional face recognition algorithms. 아니라 실내에서도 발생하고 있었다. 정량한 8개 화합물 각각과 총 휘발성 유기화합물의 스피어만 상관계수는 벤젠을 제외하고는 모두 유의하였다. 이중 톨루엔과 크실렌은 총 휘발성 유기화합물과 좋은 상관성 (톨루엔 0.76, 크실렌, 0.87)을 나타내었다. 이 연구는 톨루엔과 크실렌이 총 휘발성 유기화합물의 좋은 지표를 사용될 있고, 톨루엔, 에틸벤젠, 크실렌 등 많은 휘발성 유기화합물의 발생원은 실외뿐 아니라 실내에도 있음을 나타내고 있다.>10)의 $[^{18}F]F_2$를 얻었다. 결론: $^{18}O(p,n)^{18}F$ 핵반응을 이용하여 친전자성 방사성동위원소 $[^{18}F]F_2$를 생산하였다. 표적 챔버는 알루미늄으로 제작하였으며 본 연구에서 연구된 $[^{18}F]F_2$가스는 친핵성 치환반응으로 방사성동위원소를 도입하기 어려운 다양한 방사성의 약품개발에 유용하게 이용될 수 있을 것이다.었으나 움직임 보정 후 영상을 이용하여 비교한 경우, 결합능 변화가 선조체 영역에서 국한되어 나타나며 그 유의성이 움직임 보정 전에 비하여 낮음을 알 수 있었다. 결론: 뇌활성화 과제 수행시에 동반되는 피험자의 머리 움직임에 의하여 도파민 유리가 과대평가되었으며 이는 이 연구에서 제안한 영상정합을 이용한 움직임 보정기법에 의해서 개선되었다. 답이 없는 문제, 문제 만들기

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.6
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• pp.237-247
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• 2008

A virtual screening is the process of reducing an unmanageable number of compounds to a limited number of compounds for the target of interest by means of computational techniques such as molecular docking. And it is one of a large-scale scientific application that requires large computing power and data storage capability. Previous applications or softwares for molecular docking such as AutoDock, FlexX, Glide, DOCK, LigandFit, ViSION were developed to be run on a supercomputer, a workstation, or a cluster-computer. However the virtual screening using a supercomputer has a problem that a supercomputer is very expensive and the virtual screening using a workstation or a cluster-computer requires a long execution time. Thus we propose a service-based virtual screening system using Grid computing technology which supports a large data intensive operation. We constructed 3-dimensional chemical molecular database for virtual screening. And we designed a resource broker and a data broker for supporting efficient molecular docking service and proposed various services for virtual screening. We implemented service based virtual screening system with DOCK 5.0 and Globus 3.2 toolkit. Our system can reduce a timeline and cost of drug or new material design.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.125-148
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• 2009

UGC(User Generated Contents) is emerging as the center of e-business in the web 2.0 era. The trend reflects changing roles of users in production and consumption of contents on websites and helps us to understand new strategies of websites such as web portals and social network websites. Nowadays, we consume contents created by other non-professional users for both utilitarian (e.g., knowledge) and hedonic values (e.g., fun). Also, contents produced by ourselves (e.g., photo, video) are posted on websites so that our friends, family, and even the public can consume those contents. This means that non-professionals, who used to be passive audience in the past, are now creating contents and share their UGCs with others in the Web. Accessible media, tools, and applications have also reduced difficulty and complexity in the process of creating contents. Realizing that users create plenty of materials which are very interesting to other people, media companies (i.e., web portals and social networking websites) are adjusting their strategies and business models accordingly. Increased demand of UGC may lead to website visits which are the source of benefits from advertising. Therefore, they put more efforts into making their websites open platforms where UGCs can be created and shared among users without technical and methodological difficulties. Many websites have increasingly adopted new technologies such as RSS and openAPI. Some have even changed the structure of web pages so that UGC can be seen several times to more visitors. This mainstream of UGCs on websites indicates that acquiring more UGCs and supporting participating users have become important things to media companies. Although those companies need to understand why general users have shown increasing interest in creating and posting contents and what is important to them in the process of productions, few research results exist in this area to address these issues. Also, behavioral process in creating video UGCs has not been explored enough for the public to fully understand it. With a solid theoretical background (i.e., theory of implementation intentions), parts of our proposed research model mirror the process of user behaviors in creating video contents, which consist of intention to upload, intention to edit, edit, and upload. In addition, in order to explain how those behavioral intentions are developed, we investigated influences of antecedents from three motivational perspectives (i.e., intrinsic, editing software-oriented, and website's network effect-oriented). First, from the intrinsic motivation perspective, we studied the roles of self-expression, enjoyment, and social attention in forming intention to edit with preferred editing software or in forming intention to upload video contents to preferred websites. Second, we explored the roles of editing software for non-professionals to edit video contents, in terms of how it makes production process easier and how it is useful in the process. Finally, from the website characteristic-oriented perspective, we investigated the role of a website's network externality as an antecedent of users' intention to upload to preferred websites. The rationale is that posting UGCs on websites are basically social-oriented behaviors; thus, users prefer a website with the high level of network externality for contents uploading. This study adopted a longitudinal research design; we emailed recipients twice with different questionnaires. Guided by invitation email including a link to web survey page, respondents answered most of questions except edit and upload at the first survey. They were asked to provide information about UGC editing software they mainly used and preferred website to upload edited contents, and then asked to answer related questions. For example, before answering questions regarding network externality, they individually had to declare the name of the website to which they would be willing to upload. At the end of the first survey, we asked if they agreed to participate in the corresponding survey in a month. During twenty days, 333 complete responses were gathered in the first survey. One month later, we emailed those recipients to ask for participation in the second survey. 185 of the 333 recipients (about 56 percentages) answered in the second survey. Personalized questionnaires were provided for them to remind the names of editing software and website that they reported in the first survey. They answered the degree of editing with the software and the degree of uploading video contents to the website for the past one month. To all recipients of the two surveys, exchange tickets for books (about 5,000~10,000 Korean Won) were provided according to the frequency of participations. PLS analysis shows that user behaviors in creating video contents are well explained by the theory of implementation intentions. In fact, intention to upload significantly influences intention to edit in the process of accomplishing the goal behavior, upload. These relationships show the behavioral process that has been unclear in users' creating video contents for uploading and also highlight important roles of editing in the process. Regarding the intrinsic motivations, the results illustrated that users are likely to edit their own video contents in order to express their own intrinsic traits such as thoughts and feelings. Also, their intention to upload contents in preferred website is formed because they want to attract much attention from others through contents reflecting themselves. This result well corresponds to the roles of the website characteristic, namely, network externality. Based on the PLS results, the network effect of a website has significant influence on users' intention to upload to the preferred website. This indicates that users with social attention motivations are likely to upload their video UGCs to a website whose network size is big enough to realize their motivations easily. Finally, regarding editing software characteristic-oriented motivations, making exclusively-provided editing software more user-friendly (i.e., easy of use, usefulness) plays an important role in leading to users' intention to edit. Our research contributes to both academic scholars and professionals. For researchers, our results show that the theory of implementation intentions is well applied to the video UGC context and very useful to explain the relationship between implementation intentions and goal behaviors. With the theory, this study theoretically and empirically confirmed that editing is a different and important behavior from uploading behavior, and we tested the behavioral process of ordinary users in creating video UGCs, focusing on significant motivational factors in each step. In addition, parts of our research model are also rooted in the solid theoretical background such as the technology acceptance model and the theory of network externality to explain the effects of UGC-related motivations. For practitioners, our results suggest that media companies need to restructure their websites so that users' needs for social interaction through UGC (e.g., self-expression, social attention) are well met. Also, we emphasize strategic importance of the network size of websites in leading non-professionals to upload video contents to the websites. Those websites need to find a way to utilize the network effects for acquiring more UGCs. Finally, we suggest that some ways to improve editing software be considered as a way to increase edit behavior which is a very important process leading to UGC uploading.

• Journal of Society of Preventive Korean Medicine
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• v.24 no.2
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• pp.71-82
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• 2020

Objectives : The purpose of this study is to evaluate clinical utilization by measuring compliance with the use of mobile health applications (AtopyPHR developed in a previous study) for patients with atopic dermatitis. Methods : Based on the AtopyPHR and the input period and frequency survey results for each symptom item, a scenario for measuring compliance was derived. The study period was 4 weeks. Participants installed AtopyPHR app and Telegram app on their smartphones, conducted user training on the app, and recorded symptoms using the app for 4 weeks. At the 2nd and 4th week visits, the AtopyPHR data recorded by the user can be viewed on the web page and used for medical decision. Compliance was analyzed by the date the symptoms were recorded. Results : There were 28 participants, all (100%) were compliant, and the compliance was 96.8. The patients were 1 to 18 years old, and the average age was 8.2±5.7 years, 10 males and 18 females. The actual date of participation in recording symptoms was 28.6±0.56 on average. Compared to Week 1, compliance decreased at Week 2, and Week 4 had the highest compliance. Daily check, daily emotion, stool/urine/sleep, and meal management showed high compliance, SCORAD and quality of life were higher than required to record. Conclusions : AtopyPHR was effective in compliance. The results of this study could be used to collect personal health data in daily life through the AtopyPHR, improving participant compliance. It is considered to be meaningful because it measured the compliance with the symptom record actually recorded using the mobile app rather than a questionnaire. This study may be useful not only for personal health care but also for medical decisions, as opinions are given by experts who treat atopic dermatitis.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Korean Journal of Ecology and Environment
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• v.40 no.1
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• pp.130-142
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• 2007

This study was to examine impacts of turbid water on fish community in the downstream of Yongdam Dam during the period from June to October 2006. For the research, we selected six sampling sites in the field: two sites were controls with no influences of turbid water from the dam and other remaining four sites were the stations for an assessment of potential turbid effects. We evaluated integrative health conditions throughout applications of various models such as necropsy-based fish health assessment model (FHA), Index of Biological Integrity (IBI) using fish assemblages, and Qualitative Habitat Evaluation Index (QHEI). Laboratory tests on fish exposure under 400 NTU were performed to find out impact of turbid water using scanning electron microscope (SEM). Results showed that fine solid particles were clogging in the gill in the treatments, while particles were not found in the control. This results indicate that when inorganic turbidity increases abruptedly, fish may have a mechanical abrasion or respiratory blocking. The stream health condition, based on the IBI values, ranged between 38 and 48 (average: 42), indicating a "excellent" or "good" condition after the criteria of US EPA (1993). In the mean time, physical habitat condition, based on the QHEI, ranged 97 to 187 (average 154), indicating a "suboptimal condition". These biological outcomes were compared with chemical dataset: IBI values were more correlated (r=0.526, p<0.05, n=18) with QHEI rather than chemical water quality, based on turbidity (r=0.260, p>0.05, n=18). Analysis of the FHA showed that the individual health indicated "excellent condition", while QHEI showed no habitat disturbances (especially bottom substrate and embeddeness), food-web, and spawning place. Consequently, we concluded that the ecological health in downstream of Yongdam Dam was not impacted by the turbid water.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.