• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of Life Science
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• v.18 no.8
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• pp.1132-1139
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• 2008

A CpG island is a short stretch of DNA in which the frequency of the CG dinucleotide is higher than other regions. CpG islands are present in the promoters and exonic regions of approximately $30{\sim}60$% of mammalian genes so they are useful markers for genes in organisms containing 5-methylcytosine in their genomes. Recent evidence supports the notion that the hypermethylation of CpG island, by silencing tumor suppressor genes, plays a major causal role in cancer, which has been described in almost every tumor types. In this respect, CpG island search by computational methods is very helpful for cancer research and computational promoter and gene predictions. I therefore developed a window program (called CpGi) on the basis of CpG island criteria defined by D. Takai and P. A. Jones. The program 'CpGi' was implemented in Visual C++ 6.0 and can determine the locations of CpG islands using diverse parameters (%GC, Obs (CpG)/Exp (CpG), window size, step size, gap value, # of CpG, length) specified by user. The analysis result of CpGi provides a graphical map of CpG islands and G+C% plot, where more detailed information on CpG island can be obtained through pop-up window. Two human contigs, i.e. AP00524 (from chromosome 22) and NT_029490.3 (from chromosome 21), were used to compare the performance of CpGi and two other public programs for the accuracy of search results. The two other programs used in the performance comparison are Emboss-CpGPlot and CpG Island Searcher that are web-based public CpG island search programs. The comparison result showed that CpGi is on a level with or outperforms Emboss-CpGPlot and CpG Island Searcher. Having a simple and easy-to-use user interface, CpGi would be a very useful tool for genome analysis and CpG island research. To obtain a copy of CpGi for academic use only, contact corresponding author.

• Korean Journal of Environmental Biology
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• v.37 no.1
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• pp.19-30
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• 2019

To investigate the characteristics of seasonal environment and phytoplankton community structure in the coastal area of Dokdo, a survey of Dokdo around waters was conducted during the four seasons. Phytoplankton of 4 phylum 72 species in four seasons were collected in Dokdo around water. The seasonal mean abundance of phytoplankton were $3.32{\times}10^4cells\;L^{-1}$ in winter, $1.04{\times}10^4cells\;L^{-1}$ in spring, $0.28{\times}10^4cells\;L^{-1}$ in summer, and $4.86{\times}10^4cells\;L^{-1}$ in autumn in Dokdo around water. During winter, the diatoms Chaetoceros spp. had dominated. During spring, when the nutrients in the euphotic layer were depleted, the nano-flagellates and Cryptomonas appeared at surface layer. In summer, the abundance of phytoplankton was relatively low, which lead to occurrence of diatoms such as genus of Chaetoceros, Rhizosolenia, and Skeletonema. In autumn, Pseudo-nitzschia spp. was the most dominant species and tropical species such as Amphisolenia sp. and Ornithocercus magnificus were observed, implying that they may have introduced within warm water current such as Kurosiwo Current. Therefore, although natural phytoplankton communities in the vicinity water of Dokdo are mainly influenced by Tsushima Warm Current branched Kurosiwo Current, their population dynamics was affected on the spatio-temporal change of physicochemical factors by short-term wind events, namely "island effect". Long-term survey research is needed to facilitate food-web response in marine ecosystem associated with phytoplankton biomass and physicochemical factors including the warm water current in oligotrophic offshore water of Dokdo, which may have significant role for sustainable use of Dokdo.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of Life Science
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• v.18 no.8
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• pp.1090-1095
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• 2008

Marbling (intramuscular fat) is an important factor in determining meat quality in Korean beef market. A grain based finishing system for improving marbling leads to inefficient meat production due to an excessive fat production. Identification of intramuscular fat-specific gene might be achieved more targeted meat production through alternative genetic improvement program such as marker assisted selection (MAS). We carried out ddRT-PCR in 12 and 27 month old Hanwoo steers and detected 300 bp PCR product of the inducible cAMP early repressor (ICER) gene, showing highly gene expression in 27 months old. A 1.5 kb sequence was re-sequenced using primer designed base on the Hanwoo EST sequence. We then predicted the open reading frame (ORF) of ICER gene in ORF finder web program. Tissue distribution of ICER gene expression was analysed in eight Hanwoo tissue using realtime PCR analysis. The highest ICER gene expression showed in Small intestine followed by Longissimus dorsi. Interestingly, the ICER gene expressed 2.5 time higher in longissimus dorsi than in same muscle type, Rump. For gene expression analysis in high- and low marbled individuals, we selected 4 and 3 animal based on the muscle crude fat contents (high is 17-32%, low is 6-7% of crude fat contents). The ICER gene expression was analysed using ANOVA model. Marbling (muscle crude fat contents) was affected by ICER gene (P=0.012). Particularly, the ICER gene expression was 4 times higher in high group (n=4) than low group (n=3). Therefore, ICER gene might be a functional candidate gene related to marbling in Hanwoo.

• THE JOURNAL OF KOREAN ORIENTAL ONCOLOGY
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• v.7 no.1
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• pp.77-97
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• 2001

Anti-angiogenesis is one of therapies which have been high-lightened on the research of cancer treatment. Anti-angiogenesis means that new blood vessels are created from a existing capillary tube and it is a important process on metastasis and permeation when cancer is created or formed. Since angiogenesis have been under research, a complete recovery oriented treatment against cancer have been suggested blocking metastasis, delaying the growth of cancer cell, and blocking the supply of oxygen and nutritive substance through the web of blood vessels. Until now, there are several anti-angiogenesis, which have been known to the public, such as thalidomide, angiostatin, endostatin, 2-methoxyestradiol, TNP-470, and marimastat, etc. Additionally, 17 clinical testing projects about anti-angiogenesis are on the process in NCI(National Cancer Institute). Especially, TNP-470 showed effectiveness against cancer on clinical testing after finishing animal testing. Based on existing researches showing that Yinsamyangwui-tang is effective to strengthening body resistance and Whallakhyolenyng-dan effects cells on the inside of blood vessel because Whallakhyolenyng- dan restrains cell adhesion during the restraining period of a blood vessel, I tried to research the effect of Whalakhyolenyng-dan plus Yinsamyangwui-tang on angiogenesis. I made a conclusion putting into operation through using SK-Hep-1 (KCLB 30052), A549(KCLB 10185), AGS(KCLB 21739), and BCE(Bovine Capillary Endothelial Cell). Followings are the results of my experimental research: 1. According to the researching results of anti-cancer activation against cancer cell, Whallkhyoleyng dan plus Yinsamyangwui-tang decreased the number of cancer cells -- While injecting $600{\mu}g/ml$, injected groups decreased 3.1% more comparing with the contrastive group of SK-Hep-1, 49.7% more comparing with the contrastive group of A549, and 31.0% more comparing with the contrastive group of AGS. 2. According to the researching results of DNA composition effect between BCE and cancer cell, Whallakhyoleyng-dan plus Yinsamyangwui-tang reduced the rate of SK-Hep-1 synthesis inhibition by 59.1% at $600{\mu}g/ml$ intensity comparing with contrastive group; for A549, 72.6%; for AGS, 6.1%, for BCE, 28.9%. 3. According to the researching results about the effect of BCE cell to angiogenesis, angiogenesis was restrained at $400{\mu}g/ml$ intensity during 18 hours observation. 4. In the case of aortic ring assay, the half level of angiogenesis was reduced comparing with the contrastive group while injecting with $400{\mu}g/ml$ intensity; with $800{\mu}g/ml$, under 10% comparing with contrastive group; and with $1600{\mu}g/ml$, complete restrain. According to the above results, Whallakhyoleyng-dan plus Yinsamyangwui-tang was proved to have an anti-angiogenetic effects.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.4
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• pp.380-389
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• 2007

Stable carbon and nitrogen isotopes were analyzed in suspended particulate organic matter, macroalgae and macrobenthic invertebrates in order to determine the importance of primary organic matter sources in supporting food webs of rocky subtidal and intertidal macroalgal beds in the Korean coasts. Investigations were conducted at the inter tidal sites within Gwangyang bay, a semi-enclosed and eutrophicated bay, and the subtidal sites of the east coast, a relatively oligotrophic and open environment, in May and June 2005. Water-column suspension feeders showed more negative $\delta^{13}C$ values than those of the other feeding guilds, indicating trophic linkage with phytoplankton and thereby association with pelagic food chains. In contrast, animals of the other feeding guilds, including interface suspension feeders, herbivores, deposit feeders, omnivores and predators, displayed relatively less negative $\delta^{13}C$ values than those of the water-column suspension feeders and similar with that of macroalgae, indicating exclusive use of macroalgae-derived organic matter and association with benthic food chains. Most the macrobenthic species were considered to form strong trophic links with benthic food chains. In addition, the distribution of higher $\delta^{15}N$ values in macrobenthic consumers and macroalgae at the intertidal sites of Gwangyang Bay than those at the subtidal sites of the east coast suggests that anthropogenic nutrients may enhance the macroalgal production at the intertidal sites and in turn be incorporated into the particular littoral food web in Gwangyag Bay. These results confirm the dominant role of macroalgae in supporting rocky subtidal and intertidal food webs in the Korean coasts.

• Clinical and Experimental Pediatrics
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• v.52 no.10
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• pp.1090-1102
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• 2009

Purpose:To evaluate current feeding practices and maternal nutritional knowledge on complementary feeding. Methods:Mothers of babies aged 9-15 months who visited pediatric clinics of 14 general hospitals between September and December 2008 were asked to fill questionnaires. Data from 1,078 questionnaires were analyzed. Results:Complementary food was introduced at 4-7 months in 89% of babies. Home-made rice gruel was the first complementary food in 93% cases. Spoons were used for initial feeding in 97% cases. At 6-7 months, <50% of babies were fed meat (beef, 43%). Less than 12-month-old babies were fed salty foods such as salted laver (35%) or bean-paste soup (51%) and cow's milk (11%). The following were the maternal sources of information on complementary feeding: books/magazines (58%), friends (30%), internet web sites (29%), relatives (14%), and hospitals (4%). Compared to the 1993 survey, the incidence of complementary food introduction before 4 months (0.4% vs. 21%) and initial use of commercial food (7% vs. 39%) had decreased. Moreover, spoons were increasingly used for initial feeding (97% vs. 57%). The average maternal nutritional knowledge score was 7.5/10. Less percentage of mothers agreed with the following suggestions: bottle formula weaning before 15-18 months (68%), no commercial baby drinks as complementary food (67%), considering formula (or cow's milk) better than soy milk (65%), and feeding minced meat from 6-7 months (57%). Conclusion:Complementary feeding practices have considerably improved since the last decade. Pediatricians should advise timely introduction of appropriate complementary foods and monitor diverse information sources on complementary feeding.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.