• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.93-100
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• 2022

In the era of information overload on the Internet, the recommendation system, which is an indispensable function, is a service that recommends products that a user may prefer, and has been successfully provided in various commercial sites. Recently, studies to reflect the rating time of items to improve the performance of collaborative filtering, a representative recommendation technique, are active. The core idea of these studies is to generate the recommendation list by giving an exponentially lower weight to the items rated in the past. However, this has a disadvantage in that a time function is uniformly applied to all items without considering changes in users' preferences according to the characteristics of the items. In this study, we propose a time-aware collaborative filtering technique from a completely different point of view by developing a new similarity measure that integrates the change in similarity values between items over time into a weighted sum. As a result of the experiment, the prediction performance and recommendation performance of the proposed method were significantly superior to the existing representative time aware methods and traditional methods.

• Journal of the Society of Disaster Information
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• v.20 no.3
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• pp.533-540
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• 2024

Purpose and Method: The purpose of this study is to understand the current status of safety management following the enforcement of the Serious Accidents Punishment Act. In-depth interviews were conducted with the general managers of critical disaster work at a total of 4,555 facilities managed by 11 city-affiliated organizations under the Seoul Metropolitan Government. Through this, work-related matters are analyzed and structural causes of accidents and disasters are derived. And we would like to take improvement directions and countermeasures. Result: There is a limit to the recognition and prevention of serious accidents punishment, and improvement is needed in work overload and avoidance, communication, information management and utilization. Conclusion: As the burden of work is high, it is necessary to prohibit other tasks and concurrent positions, hire safety management experts, grant authority in line with the work, require an incentive system for safety work, and develop educational materials suitable for the ability to perform safety work.

• Archives of design research
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• v.18 no.3 s.61
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• pp.95-104
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• 2005

Although the volume and complexity of available information have increased, our ability to process such volume of complex information has not been met with corresponding development. Information designers have been given the responsibility to address such unbalanced progress by developing effective visual systems to deliver and communicate such information to the masses in a manner that is quick and easy to process and understand. This study originated in recognition of these issues. This study seeks to find a solution to these issues in rhetorics in order to proliferate visual communications in recognition of the increasing importance of information and visual communication. Rhetorics, a field of study with a long history of analyzing the delivery of communication, provides numerous possibilities for the re-establishment of importance placed on visual information communication. Included in this study are (i) a thorough analysis of the principals of expression and logic offered by rhetorics, as applicable to information design (ii) a proposal to the solution to the above-mentioned issues encompassing the rhetoric process and methods of expression of information design and (iii) the practical application of these design principals to social activities. In order to provide an example of the practical use of the rhetoric methodology Presented in this study, we applied the rhetoric methodology to the 'Information Design for Public Transportation of Seoul.' and developed a new map and a guidebook. The raw data necessary for the foregoing were obtained through the analysis of the information designs that are currently in use in connection with mass transportation in Seoul and the survey evaluation conducted among Seoul residents. We modulated the infrastructure of Seoul by using 48 TAZs, computed the routes that are most likely to be used, and proposed the predictable information analysis process. The design proposed on this study encompasses color coding and use of combined information, and application of style and sequential information analysis process.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• Information Systems Review
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• v.17 no.2
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• pp.219-238
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• 2015

As Internet and IT have been developed, people have been exposed to large amounts of information. So, many online information providers recommend relevant information to users to relieve an information overload. However, information recommendation which is based on the taste and preference of a user can lead to a problem of unbalanced information consumption. Prior research about online information has not investigated the side-effect of a recommendation function. This research suggests IT solutions for alleviating unbalanced information consumption behavior. Based on adaptation level theory and expectancy theory, we proposed self-reference point diagnosticity interface and social-reference point diagnosticity interface to help people to consume information following their own information consuming goal. We hypothesized positive impacts of these two interfaces on the self-awareness about information consuming pattern. And we predicted that self-awareness has a positive impact on the motivation and actual behavior to conform the ideal information consuming pattern which the user sets. Laboratory experiment was executed as a research method. As a result, the self-reference diagnosticity interface leaded to higher self-awareness and mitigated the unbalanced information consumption. But, the social-reference diagnosticity interface and the motivation to improve the information consuming behavior had no significant results. Academic and practical implications are discussed.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.119-140
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• 2007

Recommender systems enable a user to decide which information is interesting and valuable in our world of information overload. As the recent studies of distributed computing environment have been progressing actively, recommender systems, most of which were centralized, have changed toward a peer-to-peer approach. Collaborative Filtering (CF), one of the most successful technologies in recommender systems, presents several limitations, namely sparsity, scalability, cold start, and the shilling problem, in spite of its popularity. The move from centralized systems to distributed approaches can partially improve the issues; distrust of recommendation and abuses of personal information. However, distributed systems can be vulnerable to attackers, who may inject biased profiles to force systems to adapt their objectives. In this paper, we consider both effective CF in P2P environment in order to improve overall performance of system and efficient solution of the problems related to abuses of personal data and attacks of malicious users. To deal with these issues, we propose a multi-agent framework for a distributed CF focusing on the trust relationships between individuals, i.e. web of trust. We employ an agent-based approach to improve the efficiency of distributed computing and propagate trust information among users with effect. The experimental evaluation shows that the proposed method brings significant improvement in terms of the distributed computing of similarity model building and the robustness of system against malicious attacks. Finally, we are planning to study trust propagation mechanisms by taking trust decay problem into consideration.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.131-144
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• 2015

This research proposes a prototype for visualizing radar rainfall data using the unity 3D engine. The mashup of radar data with topographic information is necessary for the 3D visualization of the radar data with high quality. However, the mashup of a huge amount of radar data and topographic data causes the overload of data processing and low quality of the visualization results. This research utilized the Unitiy 3D engine, a widely used engine in the game industry, for visualizing the 3D topographic data such as the satellite imagery/the DEM(Digital Elevation Model) and radar rainfall data. The satellite image segmentation technique and the image texture layer mashup technique are employed to construct the 3D visualization system prototype based on the topographic information. The developed protype will be applied to the disaster-prevention works by providing the radar rainfall data with the 3D visualization based on the topographic information.

• Knowledge Management Research
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• v.21 no.3
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• pp.27-44
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• 2020

In order to solve the user's information overload problem, recommender systems infer users' preferences and suggest items that match them. The collaborative filtering (CF), the most successful recommendation algorithm, has been improving performance until recently and applied to various business domains. Visual information, such as book covers, could influence consumers' purchase decision making. However, CF-based recommender systems have rarely considered for visual information. In this study, we propose VizNCS, a CF-based deep learning model that uses visual information as additional information. VizNCS consists of two phases. In the first phase, we build convolutional neural networks (CNN) to extract visual features from image data. In the second phase, we supply the visual features to the NCF model that is known to easy to extend to other information among the deep learning-based recommendation systems. As the results of the performance comparison experiments, VizNCS showed higher performance than the vanilla NCF. We also conducted an additional experiment to see if the visual information affects differently depending on the product category. The result enables us to identify which categories were affected and which were not. We expect VizNCS to improve the recommender system performance and expand the recommender system's data source to visual information.

• Journal of The Korean Association of Information Education
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• v.7 no.1
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• pp.1-10
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• 2003

Web provides a rich learning environment for learners mainly due to the hypertext technology and the high interactivity. It provides a learner-centered learning environment. In this environment, students are to develop their own paths in non-sequential and flexible ways according to their needs and their demands. However, in many cases students who use this environment are easily exposed to difficulties in their choices of learning paths, disorientation, and cognitive overload in the cyberspace. Consequently, they have trouble in maintaining their motivation and interest in their learning. This study presents the design and the use of 3D-based space navigation system in order to maintain student's interest in educational web contents.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.33-47
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• 2015

Cellular Network assisted device-to-device (D2D) communication has been growing to reduce the overload of eNodeB and mitigate the frequency shortage. However, by sharing the uplink frequency resource with the cellular network, the interference between cellular and D2D is increased. In this paper, we propose an advanced receiver for full suppression cancellation (FSC) to reduce the interference between cellular and D2D. The proposed receiver can suppress and cancel the interference by integrating the interference rejection combining (IRC) technique with successive interference cancellation (SIC). We perform a system level simulation based on the 20-MHz bandwidth of the 3GPP LTE-A system. Simulation results show that the proposed receiver can improve SINR, throughput and spectral efficiency compared to conventional receivers.