• Informatization Policy
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• v.21 no.4
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• pp.40-58
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• 2014

This study investigated how online information affected consumers' purchases at social commerce websites. In the online purchase process, consumers use two types of online information: user generated content(UGC) and vendor generated content(VGC). These information types may influence consumers'purchase intention differently because each information builds entity trust and content trust, which play mediation roles between online information and purchase intention. According to the analysis results, general transactional information and safe transaction information of VGC and reputation information of UGC significantly affected entity trust. But content trust was affected only from general transactional information of VGC. And entity trust significantly affected content trust as well as purchase intention. These findings imply that social commerce vendors should focus mainly on entity trust for enhancing consumers' purchase intention. To achieve this objective, in the short term perspective, they should endeavor how to enhance general transaction information and safe transaction information qualities because these information types are easily controlled and improved by vendors. In the long term perspective, they should focus on reputation formation because reputation takes long time.

• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.287-309
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• 2006

While numerous studies have suggested the significance of uncertainty during the process of information-seeking, less research has investigated user uncertainty in the actual search process using a real system. This study investigated user perceptions of uncertainty in the process of the selection of Web search terms in the real information-seeking process. The subjects at the doctoral or post-doctoral level were limited to the discipline of science in order to understand user perceptions in this field. The findings revealed various dimensions, types, and incidents of uncertainty. The typology of uncertainty facilitated an understanding of the subjects' information-seeking context by identifying various aspects of the context that constituted the subjects' uncertainty The identification of two principal origins of uncertainty based on the different types of uncertainty generated implications to improve information systems and services.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1714-1721
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• 2000

In the mobile communication using DS-CDMA systems the problem of multiple user interference which reduce the performance is generated by multiple user access. In this paper to solve this problem we proposed the direct decision blind adaptive receiver with knowledge of only the desired user's spreading sequence. Simulation result present that the total user's power has equal gain The gain of signal to interference ratio for the proposed blind DD-LMS receiver has about 6[dB] than conventional receiver at additive white Gaussian noise and large gain at multipath channels. And when interference user's power has more large gain than desired user's power the gain of SIR for the proposed receiver has large value. And simulation result of bit error rate present that DD-LMS receiver has higher performance than LCCMA receiver. Thus the proposed blind DD-LMS receiver has robustness against interference of high power user and multipath channels.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.38-47
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• 2021

This study is about a blockchain-based collective quarantine management system and its commercialization model. The configuration of this system includes a biometric information transmission unit that generates biometric information based on measured values generated from wearable devices, a biometric information transmission unit that transmits biometric information generated here from a quarantine management platform, and action information transmitted from the community server. is a system including an action information receiving unit for receiving from the quarantine management platform. In addition, a biometric information receiving unit that collects biometric information from the terminal, an encryption unit that encodes biometric information generated through the biometric information receiving unit based on blockchain encryption technology, and a database of symptoms of infectious diseases to store symptom information and an infection diagnosis database. The generated database includes a location information check unit that receives from the terminal of the user identified as a symptomatic person and determines whether the user has arrived in the community based on the location information confirmation unit and the location of the user after the location is confirmed. It includes a community arrival judgment unit that judges. And, the community server helps the interaction between the generated information. Such a blockchain based collective quarantine management system can help to advance the existing quarantine management system and realize a safer and healthier society.

• Spatial Information Research
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• v.19 no.5
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• pp.13-25
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• 2011

Until now, most of the Web GIS system has been developed with one-sided service type that provides pre-built spatial information to users. Recently, however, interactive web system is getting attention because users can create directly spatial information contents that meet their needs. In line with this trend, this study had a aim to develop a UGC(User Generated Contents) support system for marine science researchers who can generate spatial data by themselves on the web. The main advantage of this system is that it provides marine survey data and marine spatial information that needed to work for marine science research. Furthermore, it provides the functions of extracting of coastline as point data for their marine study area, and making of the spatial planning map for marine field survey work and marine science thematic maps for exploratory analysis after research survey. Such kinds of interactive UGC support system gives researchers a chance for utilizing marine spatial information more easily. Therefore, it is expected that the improving of the efficiency of research works, as well as increasing of the utilization of marine spatial data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.99-105
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• 2012

Recently, there are many user selection algorithms in multi user multiple-input multiple-output (MU-MIMO) systems. One of well-known user selection methods is Semi orthogonal user selection (SUS). It is an algorithm maximizing channel capacity. However, it is applicable only when user's antenna is one. We propose a generalized user selection algorithm regardless of the number of user's antennas. In the proposed scheme, Base station (Bs) selects the first user who has the highest determinant of channel and generates a user group that correlation with first user's channel is less than allowance of correlation. Then, each determinant of channels made up of first user's channel and a user's channel in the generated group is calculated and BS selects the next user who has the highest determinant of that. BS selects following users by repeating above procedure. In this paper, we get better performance because of selecting users who have the highest determinant of channel as well as allowance of correlation optimally calculated through matrix operations.

• Journal of Information Science Theory and Practice
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• v.12 no.2
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• pp.64-78
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• 2024

Sentiment analysis is one of the promising approaches for developing a point of interest (POI) recommendation system. It uses natural language processing techniques that deploy expert insights from user-generated content such as reviews and feedback. By applying sentiment polarities (positive, negative, or neutral) associated with each POI, the recommendation system can suggest the most suitable POIs for specific users. The proposed study combines two models for POI recommendation. The first model uses bidirectional long short-term memory (BiLSTM) to predict sentiments and is trained on an election dataset. It is observed that the proposed model outperforms existing models in terms of accuracy (99.52%), precision (99.53%), recall (99.51%), and F1-score (99.52%). Then, this model is used on the Foursquare dataset to predict the class labels. Following this, user and POI embeddings are generated. The next model recommends the top POIs and corresponding coordinates to the user using the LSTM model. Filtered user interest and locations are used to recommend POIs from the Foursquare dataset. The results of our proposed model for the POI recommendation system using sentiment analysis are compared to several state-of-the-art approaches and are found quite affirmative regarding recall (48.5%) and precision (85%). The proposed system can be used for trip advice, group recommendations, and interesting place recommendations to specific users.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.1
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• pp.33-41
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• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.

• Asia pacific journal of information systems
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• v.25 no.2
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• pp.239-264
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• 2015

This study aims to investigate the effect of message content and source user identity on information diffusion in Twitter networks. For the empirical study, we collected 11,346 tweets pertaining to the three major mobile telecom carriers in Korea for three months, from September to December 2011. These tweets generated 59,111 retweets (RTs) and were retweeted at least once. Our analysis indicates that information diffusion in Twitter in terms of RT volume is affected primarily by the type of message content, such as the inclusion of corporate social responsibility activities. However, the effect of message content on information diffusion is heterogeneous to the identity of the information source. We argue that user identity affects recipients' perception of the credibility of focal information. Our study offers insights into the information diffusion mechanism in online social networks and provides managerial implications on the strategic utilization of online social networks for marketing communications with customers.