• Journal of Information Technology Services
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• v.21 no.2
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• pp.109-126
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• 2022

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.223-229
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• 2022

During the winter season, when the weather gets colder every year, electricity consumption increases rapidly. The occurrence of fires is increasing due to a short circuit in electrical facilities of buildings such as markets, bathrooms, and apartments with high population density while using a lot of electricity. The cause of these short circuit fires is mostly due to the aging of the wires, the usage increases, and the excessive load cannot be endured, and the wire sheath is melted and caused by nearby ignition materials. In this paper, the load and overheat generated in the electric wire are measured through a complex sensor composed of an overload sensor, a VoC sensor, and an overheat sensor. Based on this, big data analysis is carried out to develop a platform capable of predicting, alerting, and blocking electric fires in real time, and a simulator capable of simulated fire experiments.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.19-32
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• 2009

In this paper, we design thin-client framework capable of application sharing & data access on the Internet, and apply related skills, such as X windows system, pseudo server, CODA file system, MPI(Message Passing Interface). We suggest a framework for the thin client to access data produced by working on a server optimally as well as to run server side application, even in the case of network down. Additionally, it needed to reflect all local computing changes to remote server when network is restored. To design thin client framework with these characteristics, in this paper, we apply distributed pseudo server and CODA file system to our framework, also utilize MPI for the purpose of more efficient computing & management. It allows for implementation of network independent computing environment of thin client, also provide scalable application service to numerous user through the elimination of bottleneck on caused by server overload. In this paper, we discuss the implementing method of thin client framework in detail.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.91-96
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• 2009

In this paper, interoperability in heterogeneous wireless mesh network, and mesh nodes for providing efficient IP mobility technique offers multi-hop ARP. Heterogeneous wireless mesh networks to MANETs based on a wireless mesh network backbone and non-MANET architecture is based on a client wireless mesh network and the two mobile networks, combined with a hybrid wireless mesh network are separate. In two different hybrid wireless mesh network routing protocols used to connect the two protocols in the protocol conversion at the gateway to parallel processing problems seriously overload occurs. All of the network reliability and stability are factors that reduce. Therefore, for efficient integration with L3 routing protocols, design techniques to build ARP multi-hop go through the experiment to increase the number of mesh nodes, the packet forwarding rate and an increased hop number of the node was to ensure reliability and stability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.255-257
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• 2021

During the winter season, when electricity usage increases rapidly every year, fires are frequent due to short circuits in aging electrical facilities in multi-use facilities such as traditional markets and jjimjilbangs, apartments, and multi-family houses. Most of the causes of such fires are caused by excessive loads applied to aging wires, causing the wire covering to melt and being transferred to surrounding ignition materials. In this study, we implement a system that measures the overload and overheating of the wire through a composite sensor, detects the toxic gas generated there, and logs it to the server through the gateway. Based on this, we will develop a platform that can predict, alarm and block electric fires in real time through big data analysis, and a simulator that can simulate fire occurrence experiments.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.263-272
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• 2023

In recent times, short videos have become highly popular among college students, serving as a vibrant platform for information sharing and self-expression, while fostering a unique culture. However, their excessive consumption can lead to negative effects like reduced concentration and information overload, necessitating a balanced approach and self-control. Our study examined the impact of different characteristics of short video media on college students' organizational commitment and mental health. The findings highlighted that attributes such as accessibility and immediacy positively influence different levels of engagement, and that organizational commitment significantly affects students' mental well-being. The research underscores the importance of judicious use of short video media to positively affect college students' mental health.

• Information Systems Review
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• v.19 no.1
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• pp.49-74
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• 2017

The development of smart devices has affected employees' working environments and their lives. However, using smart devices is causing employees to experience technostress. This study aims to investigate the effects of technostress in using smart devices on usage intention in an organization. Moreover, the study investigates the effect of employees' stress-coping methods on the intention to use smart devices. This study posits familiarity, use innovativeness, role ambiguity, system vulnerability, technological limitation, and ubiquity as the antecedents of technostress. Data collected from 317 users who have experience in using smart devices in organizations are empirically tested against a research model using the PLS graph. Analysis results show that role ambiguity, system vulnerability, and technological limitation significantly influence technostress. Moreover, users take up emotion-focused coping behaviors because of technostress. Emotion-focused coping behaviors affect usage intention in organizations. However, technostress and problem-focused coping behaviors do not directly affect usage intention in organizations.

• The Journal of Industrial Distribution & Business
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• v.9 no.12
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• pp.63-72
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• 2018

Purpose - In this study, it is classified the service quality dimension of mobile shopping app using Kano model. In addition, it is evaluated quality factors suitable for strategic management from the viewpoint of service provider through mobile application through binary dimension analysis. Research design, data, and methodology - In this study, seven quality dimensions such as information quality, reliability, immediacy, convenience, design, security and customer service were derived through related studies to make binary shopping quality app quality measurement. 37 sub-variables were set by each quality dimensions. Each questionnaire was composed of positive and negative items like Kano's proposed method, and the satisfaction coefficient suggested by Timko(1993) was examined to understand the influence of each factors on customer satisfaction. Results - As a result of research, shopping app users perceived unity quality factor in most items of service quality dimension such as information quality, reliability, immediacy, convenience and customer service. In addition, the satisfaction coefficient showed a good impression, quick response of the result, fast delivery, and the unsatisfactory coefficient showed more interest in personal information such as payment method safety, and transaction security. As a result of research, shopping app users perceived unity quality factor in most items of service quality dimension such as information quality, reliability, immediacy, convenience and customer service. And, in information quality, the information overload was classified as an apathetic quality component, while the related information provision belonged to an attractive quality component. In reliability quality, customized service provision was classified as an attractive quality component. In instant connectivity, the quality of the connection during transport was classified as an attractive quality component. In convenience quality, access to product information was classified as a one-way quality component. All components of designs quality were classified as attractive quality components, and in security quality, all of their components were all classified as one quality component. Lastly, in customer service, they components were all classified as a single quality component. In addition, the satisfaction coefficient showed a good impression, quick response of the result, fast delivery, and the unsatisfactory coefficient showed more interest in personal information such as payment method safety, and transaction security. Conclusion - In the online service environment, which is difficult to differentiate in terms of upward upgrading only by technological implementation and function, the results of this study can be suggested as a differentiating factor for major channels with customers rather than improve the brand image.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.15-28
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• 2015

As personal devices and pervasive technologies for interacting with networked objects continue to proliferate, there is an unprecedented world of scattered pieces of contextualized information available. However, the explosive growth and variety of information ironically lead users and service providers to make poor decision. In this situation, recommender systems may be a valuable alternative for dealing with these information overload. But they failed to utilize various types of contextual information. In this study, we suggest a methodology for context-aware recommender systems based on the concept of contextual boundary. First, as we suggest contextual boundary-based profiling which reflects contextual data with proper interpretation and structure, we attempt to solve complexity problem in context-aware recommender systems. Second, in neighbor formation with contextual information, our methodology can be expected to solve sparsity and cold-start problem in traditional recommender systems. Finally, we suggest a methodology about context support score-based recommendation generation. Consequently, our methodology can be first step for expanding application of researches on recommender systems. Moreover, as we suggest a flexible model with consideration of new technological development, it will show high performance regardless of their domains. Therefore, we expect that marketers or service providers can easily adopt according to their technical support.

• Journal of Information Management
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• v.41 no.4
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• pp.227-249
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• 2010

Scholarly information has increased tremendously according to the development of IT, especially the Internet. However, simultaneously, people have to spend more time and exert more effort because of information overload. There have been many research efforts in the field of expert systems, data mining, and information retrieval, concerning a system that recommends user-expected information items through presumption. Recently, the hybrid system combining a content-based recommendation system and collaborative filtering or combining recommendation systems in other domains has been developed. In this paper we resolved the problem of the current recommendation system and suggested a new system combining collaborative filtering and Naive Bayes Classification. In this way, we resolved the over-specialization problem through collaborative filtering and lack of assessment information or recommendation of new contents through Naive Bayes Classification. For verification, we applied the new model in NDSL's paper service of KISTI, especially papers from journals about Sitology and Electronics, and witnessed high satisfaction from 4 experimental participants.