• The Journal of the Korea Contents Association
• /
• v.10 no.12
• /
• pp.457-465
• /
• 2010

Customized information analysis system is a software system that can help to extract useful information from non-structured natural language data, process the information to customized form, and provide future forecast and reasoning information. To implement the information analysis system, we need natural language processing technology to analyze natural language, information extraction technology to detect necessary entity and its relationship from text, and data mining technology to discover new and unknown information from extracting data. This paper suggest virtual customized information analysis system processing national defense news data and introduce base technologies for information analysis.

• International journal of advanced smart convergence
• /
• v.10 no.3
• /
• pp.131-142
• /
• 2021

Recently, there has been an active service that provides customized news to news subscribers. In this study, we intend to design a customized news service system through Deep Learning-based Social Network Service (SNS) activity analysis, applying real news and avoiding fake news. In other words, the core of this study is the study of delivery methods and delivery devices to provide customized news services based on analysis of users, SNS activities. First of all, this research method consists of a total of five steps. In the first stage, social network service site access records are received from user terminals, and in the second stage, SNS sites are searched based on SNS site access records received to obtain user profile information and user SNS activity information. In step 3, the user's propensity is analyzed based on user profile information and SNS activity information, and in step 4, user-tailored news is selected through news search based on user propensity analysis results. Finally, in step 5, custom news is sent to the user terminal. This study will be of great help to news service providers to increase the number of news subscribers.

• Fashion & Textile Research Journal
• /
• v.24 no.4
• /
• pp.460-470
• /
• 2022

Customized cosmetics are emerging as a consumer product that companies should pay attention to in the beauty industry due to the combination of market trends and institutional introduction of customized cosmetics. In this study, six offline service brands and online service brands currently in Korea were selected to understand the current status of domestic customized cosmetics online and offline services and to derive detailed characteristics, and the cases of each brand were analyzed. The results are as follows. First, customized cosmetics services could be classified online and offline. Second, customized cosmetics brands could be divided into general brand types and brand extension types. Third, skin data measurements could be classified into genetic analysis, big data-based surveys, and device measurements. Fourth, customized cosmetics manufacturing could be classified into a device manufacturing system, a consultant manufacturing system, and an individual production process system. Fifth, customized cosmetics distribution and delivery could be classified into same-day sales, general delivery, and regular delivery. The results of this study are meaningful in that they have identified and analyzed the current status of personalized cosmetics on-line and offline systems in recent trends, and it was confirmed that creative attempts in the domestic customized cosmetics market continue to change. It is hoped that this study will provide information and ideas to the beauty industry and related experts in the future and be used as basic data for customized cosmetics marketing

• Journal of Korea Spatial Information System Society
• /
• v.11 no.4
• /
• pp.28-32
• /
• 2009

For the ubiquitous web environment internet, as an one of the R&D projects by Korean Land Spatialization Group, the project for platform technologies was initiated to provide customized land information and geospatial service. The platform technologies can be categorized for streaming, mashup and geosearch. More specifically, the 2D/3D hybrid streaming engine, mashup engine for u-GIS service and next generation search engine for land information have been developed. In this context, the patent analyses are required to propose the strategies for efficient development and use of these technologies. This study searches the related patents in Korea, US and EU, analyzes the trend of them for customized spatial information technologies, and finally proposes proper strategies.

• Journal of Korea Technology Innovation Society
• /
• v.20 no.1
• /
• pp.81-102
• /
• 2017

Financing, manpower support and tax are the most popular tools for policy for small and medium enterprises (SMEs). This paper, however, will introduce information analysis support for SMEs and will prove that can be a good tool. The information analysis support means the support of technology and market information for the technology development or commercialization of SMEs. Therefore, the support is a customized one. In the theory domain, we adopt and prove two theoretical grounds as an SMEs policy such as market and system failure. In the policy tool domain, we suggest four requirements to be an SMEs policy and prove the tool to satisfy these requirements. All the data and proofs are from a government research institute called K.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.4
• /
• pp.537-542
• /
• 2021

Smart TVs provide a variety of services and information compared to existing TVs based on the Internet. In order to provide more personalized services or information, it is necessary to analyze users' viewing patterns and provide customized services or information based on them. The proposed system receives the user's TV viewing pattern, analyzes it, and recommends a TV program or movie as customized information to the user. For this, the system was constructed with a preprocessor and a deep learning model. The preprocessor refines the name of the TV program watched by the user, the date the TV program was watched, and the watched time. Then, the multi-attribute LSTM model trains the refined data and performs prediction.The proposed system is a system that provides customized information to users, and is believed to be a leading technology in digital convergence that combines existing IoT technology and deep learning technology.

• Journal of Korea Multimedia Society
• /
• v.25 no.8
• /
• pp.1109-1121
• /
• 2022

As the demand for quality sleep increases in modern society, the importance of sleep technology has increased. Recently, development of sleep environment improvement products and research on the user's sleep improvement have been activated. Representatively, user sleep pattern analysis research is being conducted through the existing polysomnography, but it is difficult to use it in the sleep environment of daily life. Therefore, in this paper, we propose a system design that can provide a customized deep sleep service to users by detecting sleep disturbance factors in a motion bed environment. In order to improve the user's sleep satisfaction, a logistic regression-based sleep pattern analysis model is proposed and accuracy and significance are verified through experiments. And to improve user's sleep satisfaction, we propose a logistic regression-based sleep pattern analysis model and verify accuracy and significance through experiments. The proposed system is expected to improve the user's sleep quality and effectively prevent and manage sleep disorders.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.97-113
• /
• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.460-461
• /
• 2018

The traditional recommendation system is developed on the assumption that users behave independently, and have problem of readability and efficiency are inferior due to simply sort products or lack of function for associate product attributes with user's taste. To solve this problem in this study we propose a system that provides user customized information that the analysis of the unstructured review data with the purchase histories of users processed with meaningful information after crawling product review data using text mining with R. This allows to help user make decisions can be provided only necessary information without analyze massive amounts of products review data.

• Fashion & Textile Research Journal
• /
• v.17 no.1
• /
• pp.102-115
• /
• 2015

Due to the diversification of lifestyles and the rapid growth of Internet environments since the 1990s, mass customization has been recently accepted as an important trend in the area of clothing and all other areas. In response to mass customized clothing products, global clothing product brands are introducing systems for mass customization such as the application of digital fashion systems that introduced IT technologies such as CAD and 3D scanners. However, studies of planning factors for clothing products applied with digital fashion systems in the area of mass production of clothing products are insufficient. Therefore, this study was intended to analyze the lifestyles of 20-30s that are expected to have the highest demand for clothing applied with digital fashion systems and present basic planning factors according to lifestyles. Through the analysis, three groups that have one of fashion pursuing type, sensory information pursuing type, and practical function pursuing type lifestyles were derived. Based on this result, consumer demand for digital fashion systems and basic factors for product planning were analyzed to present basic planning factors for digital fashion system based customized clothing by lifestyle group. This study is meaningful in that it provided basic data for product planning through digital fashion systems by analyzing the awareness, preference, necessity, and planning factors of digital fashion systems through the analysis of lifestyle types.