• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.39-58
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• 2014

As the importance of big data and related technologies continues to grow in the industry, it has become highlighted to visualize results of processing and analyzing big data. Visualization of data delivers people effectiveness and clarity for understanding the result of analyzing. By the way, visualization has a role as the GUI (Graphical User Interface) that supports communications between people and analysis systems. Usually to make development and maintenance easier, these GUI parts should be loosely coupled from the parts of processing and analyzing data. And also to implement a loosely coupled architecture, it is necessary to adopt design patterns such as MVC (Model-View-Controller) which is designed for minimizing coupling between UI part and data processing part. On the other hand, big data can be classified as structured data and unstructured data. The visualization of structured data is relatively easy to unstructured data. For all that, as it has been spread out that the people utilize and analyze unstructured data, they usually develop the visualization system only for each project to overcome the limitation traditional visualization system for structured data. Furthermore, for text data which covers a huge part of unstructured data, visualization of data is more difficult. It results from the complexity of technology for analyzing text data as like linguistic analysis, text mining, social network analysis, and so on. And also those technologies are not standardized. This situation makes it more difficult to reuse the visualization system of a project to other projects. We assume that the reason is lack of commonality design of visualization system considering to expanse it to other system. In our research, we suggest a common information model for visualizing text data and propose a comprehensive and reusable framework, TexVizu, for visualizing text data. At first, we survey representative researches in text visualization era. And also we identify common elements for text visualization and common patterns among various cases of its. And then we review and analyze elements and patterns with three different viewpoints as structural viewpoint, interactive viewpoint, and semantic viewpoint. And then we design an integrated model of text data which represent elements for visualization. The structural viewpoint is for identifying structural element from various text documents as like title, author, body, and so on. The interactive viewpoint is for identifying the types of relations and interactions between text documents as like post, comment, reply and so on. The semantic viewpoint is for identifying semantic elements which extracted from analyzing text data linguistically and are represented as tags for classifying types of entity as like people, place or location, time, event and so on. After then we extract and choose common requirements for visualizing text data. The requirements are categorized as four types which are structure information, content information, relation information, trend information. Each type of requirements comprised with required visualization techniques, data and goal (what to know). These requirements are common and key requirement for design a framework which keep that a visualization system are loosely coupled from data processing or analyzing system. Finally we designed a common text visualization framework, TexVizu which is reusable and expansible for various visualization projects by collaborating with various Text Data Loader and Analytical Text Data Visualizer via common interfaces as like ITextDataLoader and IATDProvider. And also TexVisu is comprised with Analytical Text Data Model, Analytical Text Data Storage and Analytical Text Data Controller. In this framework, external components are the specifications of required interfaces for collaborating with this framework. As an experiment, we also adopt this framework into two text visualization systems as like a social opinion mining system and an online news analysis system.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.129-143
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• 2012

Recently, the rapid progress of a number of standardized web technologies and the proliferation of web users in the world bring an explosive increase of producing and consuming information documents on the web. In addition, most companies have produced, shared, and managed a huge number of information documents that are needed to perform their businesses. They also have discretionally raked, stored and managed a number of web documents published on the web for their business. Along with this increase of information documents that should be managed in the companies, the need of a solution to locate information documents more accurately among a huge number of information sources have increased. In order to satisfy the need of accurate search, the market size of search engine solution market is becoming increasingly expended. The most important functionality among much functionality provided by search engine is to locate accurate information documents from a huge information sources. The major metric to evaluate the accuracy of search engine is relevance that consists of two measures, precision and recall. Precision is thought of as a measure of exactness, that is, what percentage of information considered as true answer are actually such, whereas recall is a measure of completeness, that is, what percentage of true answer are retrieved as such. These two measures can be used differently according to the applied domain. If we need to exhaustively search information such as patent documents and research papers, it is better to increase the recall. On the other hand, when the amount of information is small scale, it is better to increase precision. Most of existing web search engines typically uses a keyword search method that returns web documents including keywords which correspond to search words entered by a user. This method has a virtue of locating all web documents quickly, even though many search words are inputted. However, this method has a fundamental imitation of not considering search intention of a user, thereby retrieving irrelevant results as well as relevant ones. Thus, it takes additional time and effort to set relevant ones out from all results returned by a search engine. That is, keyword search method can increase recall, while it is difficult to locate web documents which a user actually want to find because it does not provide a means of understanding the intention of a user and reflecting it to a progress of searching information. Thus, this research suggests a new method of combining ontology-based search solution with core search functionalities provided by existing search engine solutions. The method enables a search engine to provide optimal search results by inferenceing the search intention of a user. To that end, we build an ontology which contains concepts and relationships among them in a specific domain. The ontology is used to inference synonyms of a set of search keywords inputted by a user, thereby making the search intention of the user reflected into the progress of searching information more actively compared to existing search engines. Based on the proposed method we implement a prototype search system and test the system in the patent domain where we experiment on searching relevant documents associated with a patent. The experiment shows that our system increases the both recall and precision in accuracy and augments the search productivity by using improved user interface that enables a user to interact with our search system effectively. In the future research, we will study a means of validating the better performance of our prototype system by comparing other search engine solution and will extend the applied domain into other domains for searching information such as portal.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.147-165
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• 2008

IT education can be summarized as persuading the educatee to accept IT. The persuasion is made by delivering the messages for how-to-use and where-to-use to the educatee, which leads formulation of a belief structure for using IT. Therefore, message based persuasion theory, as well as IT acceptance theories such as technology acceptance model(TAM), would play a very important role for explaining IT education. According to elaboration likelihood model(ELM) that has been considered as one of the most influential persuasion theories, people change attitude or perception by two routes, central route and peripheral route. In central route, people would think critically about issue-related arguments in an informational message. In peripheral route, subjects rely on cues regarding the target behavior with less cognitive efforts. Moreover, such persuasion process is not a one-shot program but continuous repetition with feedbacks, which leads to changing a belief structure for using IT. An educatee would get more knowledge and experiences of using IT as following an education program, and be more dependent on a central route than a peripheral route. Such change would reformulate a belief structure which is different from the intial one. The objectives of this study are the following two: First, an identification of the relationship between ELM and belief structures for using IT. Especially, we analyze the effects of message interpretation through both of central and peripheral routes on perceived usefulness which is an important explaining variable in TAM and perceived use control which have perceived ease of use and perceived controllability as sub-dimensions. Second, a longitudinal analysis of the above effects. In other words, change of the relationship between interpretation of message delivered by IT education and beliefs of IT using is analyzed longitudinally. For achievement of our objectives, we suggest a research model, which is constructed as three-layered. While first layer has a dependent variable, use intention, second one has perceived usefulness and perceived use control that has two sub-concepts, perceived ease of use and perceived controllability. Finally, third one is related with two routes in ELM, source credibility and argument quality which are operationalization of peripheral route and central route respectively. By these variables, we suggest five hypotheses. In addition to relationship among variables, we suggest two additional hypotheses, moderation effects of time in the relationships between perceived usefulness and two routes. That is, source credibility's influence on perceived usefulness is decreased as time flows, and argument quality's influence is increased. For validation of it, our research model is tested empirically. With measurements which have been validated in the other studies, we survey students in an Excel class two times for longitudinal analysis. Data Analysis is done by partial least square(PLS), which is known as an appropriate approach for multi-group comparison analysis with a small sized sample as like this study. In result. all hypotheses are statistically supported. One of theoretical contributions in this study is an analysis of IT education based on ELM and TAM which are considered as important theories in psychology and IS theories respectively. A longitudinal analysis by comparison between two surveys based on PLS is also considered as a methodological contribution. In practice, finding the importance of peripheral route in early stage of IT education should be notable.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.331-336
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• 2004

In order to strengthen the competitiveness of port against calling for the huge vessel and reducing the shipping service time, the productivity of container terminal must be improved. This productivity variously results according to the kinds of productivity evaluation model, input elements like yard, equipment, employee, facility, etc,. But, it is discussed that the productivity is measured by partial productivity evaluation model or general input elements. Therefore, we measured for the productivity of the container terminal using the Developed the data Envelopment Analysis (DEA), which is developed in order to evaluate the relative efficiency of decision making units - it's difficult to clear cause and effect between input and output. We measured the whole productivity of container terminal in Busan according to decision of the correct input elements. And we investigated the change of the productivity measurement result according to input elements, presents more accurate productivity evaluation model in container terminal.

• Information Systems Review
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• v.12 no.1
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• pp.107-130
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• 2010

Knowledge management strategy is considered a key determinant of successful knowledge management. However, theoretical and empirical researchers disagree on how knowledge management strategies improve firms' performance. The inconsistent results of prior studies may be attributed to the fact that complementary relationships among knowledge management strategies have not been adequately taken into consideration. While the previous literature has focused on investigating the impact of knowledge sourcing strategies on firms' performance one-at-a-time, in reality firms adopt several different knowledge management strategies together. By drawing on complementarity theory, this study revisits this research problem and develops three complementarity hypotheses. Surveys collected from 139 firms in Korea were analyzed to test the hypotheses by using super modularity function. The results confirmed complementary relationships between system- and person-oriented, and between internal- and external-oriented knowledge management strategies. Our results found no complementarity among the four different knowledge management strategies. This study sheds new light on knowledge management research by developing a new conceptual framework and using advanced analytical approaches to explore the relationship between knowledge management strategies and firms' performance. Implications for practice highlight that a successful knowledge management strategy requires a judicious combination of system- and person-oriented, or of internal- and external-oriented knowledge management strategies.

• Information Systems Review
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• v.13 no.3
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• pp.165-183
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• 2011

IT organizations have been consistently required to change in order to cope with business and technological changes. However, the practical methodologies or guidelines about IT organization redesign are deficient. This research investigates IT organization's redesign methods and procedures focused on K public corporation which fundamentally redesigned IT organization recently. In K public corporation, to-be model of IT organization was designed with job analysis for IT organization redesign, task redesign was performed, and IT organization and personnel was assessed. 1) In task analysis, core activities are identified and 96 standard tasks are drawn. 2) with to-be organization design, IT support, IT delivery, IT operation teams and BuKyoung and Jeju teams were divided. 3) Previous job organization was restructured, IT organization personnel were finally confirmed through FTE and internal review. K public corporation strengthened IT planning task instead of reducing operational IT task, improved IT management process which was lower than other companies, and improved IT outsourcing management system and IT users' satisfactions. This research has practical implications for many companies which struggle with IT organization redesign method and process.

• Management & Information Systems Review
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• v.30 no.1
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• pp.107-128
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• 2011

This study has two primary purposes, firstly to identify how job dissatisfaction and continuance commitment influence to creativity, secondly to explore how coworker helping and support, and perceived organizational support moderate between interaction of job dissatisfaction and continuance commitment, and creativity. The first part of the study, based on literature study on creativity, provides insight into what are antecedents and moderate variables in creativity. In the second part of the study, a comprehensive research model and hypothesis were empirically tested based on data from 322 employees in Korean organizations. The results of statistical analysis show the following. First, job dissatisfaction has positive effect on creativity. Second, interaction of job dissatisfaction and continuance commitment does not have positive effect on creativity. Third, there was not any moderating effects between interaction of job dissatisfaction and continuance commitment, and creativity in this study. The last part of this study, a theoretical and practical implication of the study, and the future research agenda are presented.

• Management & Information Systems Review
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• v.34 no.5
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• pp.295-312
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• 2015

Companies are recently emphasizing social capital that is formed by the network and trust among organization members to secure continuous competitive edge. Social capital induces the members' adaptation and immersion through the interactions with multidimensional factors within an organization, and contributes to increasing an organization's performance by causing cooperative behaviors as a passage of communications and participation. This study analyzed the influence of social capital and organizational support recognition formed in an organization on the turnover intention, and examined the moderating effect of organizational support recognition in the relationship between social capital and turnover intention. To achieve the purpose, this research conducted a survey on small and medium sized manufacturing companies in Busan and Gyeongnam and performed an empirical analysis using hierarchical regression analysis. According to the empirical analysis, the structural and relational dimensions of social capital had a negative (-) influence on the turnover intention. Especially, the relational dimension had a huge influence on the turnover intention, showing that it is important to form trust among an organization's members through their interactions. Second, organizational support recognition also had a negative (-) influence on the turnover intention, demonstrating that attention and complete support at an organizational dimension were needed for individual members. Third, organizational support recognition appeared to mediate the relationship between social capital and the turnover intention. The higher the organizational support recognition was, the lower the negative (-) influence of the relational dimension of social capital on the turnover intention was. Based on these results, this paper discussed the theoretical and practical implications of this research as well as future assignments.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.3
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• pp.77-94
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• 2017

This Paper is the Thesis that Reports Empirical Results on the Effect of Maintaining Behavior of Native Culture on the Job Attitude of Immigrant Labor and the Mediating Effect of Psychological Well-being. In order to Accomplishment this Goal, an Empirical Study was Implemented. The Data for an Empirical Study was Obtained from the Survey on 205 Immigrant Labor from Vietnam in Busan and Gyeongsangnam-do. And Multiple Regression Analysis was Conducted to Examine the Research Hypothesis. The Summary of Empirical Study's Results is Follows. 1) Among Maintaining Behavior of Native Culture, Native Culture-related Media Behavior, Meeting with Native Companions and Persistence of Native Religious Activity have the Positive Effect on the Job Attitude Significantly. 2) Eating Native Food have the Negative Effect on the Job Satisfaction Significantly. 3) There are Mediating Effects of Psychological Well-being on the Relationship Between the Maintaining Behavior of Native Culture and Job Attitudes. Maintaining Behavior of Native Culture has Significant Positive Effect on Job Involvement and Job Satisfaction Indirectly Via Mechanisms Such as the Spillover Effect of Psychological Well-being. That is, The more Immigrant Workers do Maintaining Behavior of Native Culture, the Higher Psychological Well-being is and it Improves Their Job Involvement and Job Satisfaction. The Implication of these Results, the Limitation of this Study and the Direction of Future Study were Suggested.