• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.106-114
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• 2019

The study was conducted to investigate the effect of whole-wheat ratio and nutrition information provision on purchasing behavior and consumer intention among individuals in their twenties who lack calcium intake. In the end, this study aims to provide basic data on the development and marketing strategy of customized nutrition-reinforced snacks. Regarding whole wheat ratios, the acceptance of taste of whole wheat flour was highest in ZF but didn't seem significant difference with HF's. The familiarity of taste and flavor (P <0.001), purchase intention (p <0.001) and recommendation intention (p <0.001) were higher in order of ZF, HF and TF. Regarding information provision, familiarity of taste and flavor (P <0.05), purchase intention (p <0.05) and recommendation intention (p <0.05) were higher in order of detailed information group, non - information group and simple information group. Therefore, developing calcium-fortified biscuit, mixing whole wheat flour with normal flour might reduce consumer's resistance, Also, providing detailed information on the degree of fortification of calcium and dietary fiber might cause a synergistic effect on consumption.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.155-178
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• 2021

In the era of the 4th industrial revolution, public libraries need a strategy for promoting intelligent library services in order to actively respond to changes in the external environment such as artificial intelligence. Therefore, in this study, based on the concept of artificial intelligence and analysis of domestic and foreign artificial intelligence related trends, policies, and cases, we proposed the future direction of introduction and development of artificial intelligence services in the library. Currently, the library operates a reference information service that automatically provides answers through the introduction of artificial intelligence technologies such as deep learning and natural language processing, and develops a big data-based AI book recommendation and automatic book inspection system to increase business utilization and provide customized services for users. Has been provided. In the field of companies and industries, regardless of domestic and overseas, we are developing and servicing technologies based on autonomous driving using artificial intelligence, personal customization, etc., and providing optimal results by self-learning information using deep learning. It is developed in the form of an equation. Accordingly, in the future, libraries will utilize artificial intelligence to recommend personalized books based on the user's usage records, recommend reading and culture programs, and introduce real-time delivery services through transport methods such as autonomous drones and cars in the case of book delivery service. Service development should be promoted.

• The Journal of Korean Academy of Prosthodontics
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• v.38 no.3
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• pp.336-347
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• 2000

Statement of problem. Luster loss in esthetic anterior ceromer restoration can occur and can be related with rough surface texture. Understanding durability of surface finishing methods like polishing and surface coating have critical importance. Purpose. This study evaluated the effect of tooth brushing and thermal cycling on surface luster of 3 ceromer systems (Artglass, Targis, Sculpture) treated with different surface finishing methods. Material and methods. Seventy-two samples were prepared: 12 for control group Z100, 12 for Artglass, 24 for Targis, and 24 for Sculpture. Half of the Targis and Sculpture were polished according to the manufacturer's recommendation. The rest of the samples were coated with staining and glazing solution for Targis and Sculpture, respectively. All specimens were subjected to 10,000 cycles between $5^{\circ}C\;and\;55^{\circ}C$ with 30 seconds dwell time. Tooth brushing abrasion tests were performed in a customized tooth brushing machine with 500g back and forth for 20,000 cycle. Luster comparisons were based on grading after direct observation, and light reflection area was measured with Image analysis software. Results. All materials showed an decrease in luster grade after thermal cycling and tooth brushing. The post-tooth brushing results revealed that the glazed Sculpture had greater mean luster grade than did any other groups. While, the stained Targis group showed greatest changes after tooth brushing (p < 0.05), polished Targis and Sculpture did not show significant changes. However, glazed Sculpture showed discretely fallen out glaze resin. Conclusion. From the results of this study, all of the ceromer specimens were much glossy than control composite group after tooth brushing. coatings used for Targis and Sculpture had not durability for long term use.

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.25-32
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• 2018

Recently, the size and type of disasters in Korea has been diversified. However, Korea has not been able to build various information support systems to predict these disasters.Many other organizations also provide relevant information. This information is mainly provided on the Web, but most of it is not real time information. In this study, we have paid attention to support information using big data to provide better quality real - time information together with information provided by institutions. Big data has a large amount of information with real-time property, and it can make customized service using it. Among them, SNS such as Twitter and Facebook can be used as a new information collection medium in case of disaster. However, it is very difficult to retrieve necessary information from too much information, and it is difficult to collect intuitive information. For this purpose, this study develops an information support system using Twitter. The system retrieves information using the Twitter hashtag. Also, information mapping is performed on the map so that intuitive information can be grasped. For system evaluation, information extraction, degree of mapping, and recommendation speed are evaluated.

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.17-22
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• 2015

High School and College Bridge Program for high school students to learn about specialty training through Mentors with high tech equipment before college admissions has been established. In this study we further developed the previous research experience in 2013 to mentoring program. According to this program, we developed the team learning program and then conducted analytic study on the second year achievement. They became mentees and undergraduate students or graduate course student were mentors. High school students learned how to solve problems by themselves under the mentoring education. The mentees had higher satisfaction in lecture and interest increasing factors at first part of the program. In second part, they showed more satisfaction in new knowledges and recommendation factors. The relationship and intimacy have grown through interaction between mentors and mentees during the team learning. The high school and college bridge program would have significant meaning to develope a customized program for high school students through continuous study.

• Journal of the Korean Society of Food Culture
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• v.33 no.4
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• pp.394-401
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• 2018

This study was conducted to investigate the effects of health lifestyle on high protein snack selection attributes and purchase behaviors among individuals aged 20-30 with high protein snack intake. In addition, the relationship between perception, attitude, satisfaction and recommendation of high protein snacks was invested. Finally, this study aims to provide basic information for marketing high-protein snacks and customized high protein snacks. Analysis of the selection attributes most important for healthy lifestyle, revealed significant differences among all groups excluding the external seeking group (p<0.001). The free living group regarded trust as one of the most important attributes of high protein snack selection, and both the tempered control group and the low-interest group found sensation and price factor to be important. Therefore, when developing high-protein snacks, it is important to determine which attributes of the snack will be highlighted by segmenting the consumer into health lifestyles. Focusing on what ingredients are used to develop high-protein snacks and nutritional ingredients is also important when targeting a free lifestyle group as the main customer. In addition, developing snacks that do not offer depending on the protein content is important when targeting a temperate management group or a low-interest group.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.117-123
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• 2013

The development of the latest emotional recognition and multimedia technology has changed the traditional broadcasting system. The previous broadcasting system, which was operated by the terrestrial broadcasters, is now transformed to the viewer-centered and bidirectional broadcasting through the convergence of internet, mobile and smart TV. In this paper, smart-phone application for estimating human emotion(sadness, anger, depression) has been developed and emerged with smart TV, thereby we can present broadcasting service for enhancing the sense of common humanity among people of same group. If there is friend in the depression, we can bring comfort to him by inviting one for TV program what I watch and having a honest talk with facial avatar or emoticon. The proposed emotional broadcasting service inter-working with smart-phone application can give feeling of belonging and happiness to the people suffering from the blues, and it can prevent him from attempting suicide. In addition, smart-phone based emotional broadcasting service can be expended to program recommendation service customized to user's emotion, emotional LED lighting service to maximize the sense of reality and home shopping service taking advantage of the mood of customer.

• Journal of Information Technology Applications and Management
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• v.22 no.1
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• pp.77-93
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• 2015

As the range of the customer choice becomes more diverse, the average life span of companies' products and services is becoming shorter. Most companies are striving to maximize the revenue by understanding the customer's needs and providing customized products and services. However, companies had to bear a significant burden, in terms of the time and cost involved in the process of determining each individual customer's needs. Therefore, an alternative method is employed that involves grouping the customers into different categories based on certain criteria and establishing a marketing strategy tailored for each group. In this way, customer segmentation and customer clustering are performed using demographic information and behavioral information. Demographic information included sex, age, income level, and etc., while behavioral information was usually identified indirectly through customers' purchase history and search history. However, there is a limitation regarding companies' customer behavioral information, because the information is usually obtained through the limited data provided by a customer on a company's website. This is because the pattern indicated when a customer accesses a particular site might not be representative of the general tendency of that customer. Therefore, in this study, rather than the pattern indicated through a particular site, a customer's interest is identified using that customer's access record pertaining to external news. Hence, by utilizing this method, we proposed a methodology to perform customer segmentation. In addition, by extracting the main issues through a topic analysis covering approximately 3,000 Internet news articles, the actual experiment applying customer segmentation is performed and the applicability of the proposed methodology is analyzed.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.111-119
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• 2010

U-Healthcare provides healthcare and medical services, such as prevention, diagnosis, treatment, and follow-up services whenever and wherever it is needed, and its ultimate goal is to improve quality of life. This study defines the figure of U-Healthcare personalized services for providing U-Healthcare personalized services and proposes a healthcare model. A diet prescription system for personalized services can draw customized calories and rates of nutrition factors and represent a personalized diet through analyzing the personal preference in foods. This system changes the personal preference by monitoring the diet selection behavior of users. Also, this system is designed to be interactively operated with some sensors and devices in various environments using Java-based OSGi middleware.