• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Journal of Korean Academy of Nursing
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• v.15 no.1
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• pp.17-29
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• 1985

This study was to explore on the rank of stressful events related to the experience of hospitalization. 180 hospitalized patients on surgical and medical wards were asked to rate 49 stress-producing events associated with the experience of hospitalization. Two university hospitals was used as the setting for this study. Because the nature of the events in the stress scale pertain mainly to general short term hospitalizations, patients in the rehabilitation and psychiatric units of the hospital were not included. Prior to the beginning of the study, three times meeting were held with 12 head nurses and 3 investigators for discussing with the ethics subject related to the study. The pretest was done to determine whether items to use were pertinent or not. According to the result of the pretest, Volicer's Hospital Stress Rating Scale was selected as a study tool for this study. Data collection was used an interview and a card-sorting method. The interviewing was done by two authors and three graduate nursing students. A total 125 completed the card-sorting procedure. The stressful items were ordered from most to least stressful within the categories. Additional information such as: age, sex, marital status, and diagnosis was obtained from the kardex file. The ordered list of items, with mean values, as scored by the total of 125 respondents was significantly accepted at 1% level by Friedman test. (X²=1448.339) The event,“knowing you have a serious illness.”was rated highest stressful and (M=41.54) “Being awakened in the night by the nurse”least stressful. (M=14.73) Highly rated items were orderly “Thinking you might have cancer”“Thinking you might lose a kidney or some other organ”“Not being told what your diagnosis is. “Not knowing for sure what illness you have,”five lowerly rated items were orderly “Having to eat at different times than you usually do”“net being able to call family or friends on the phone”“Not having friends visit you,”“Having strangers sleep in the same room with you.”Futher analysis of the data was done to ascertain tao degree of similarity of judgment between different groups in the sample as to how events should be rated. The sample was divided into two groups according to the demographic characteristics and the degree of seriousness of illness. The rank order correlation was calculated for the two sets of ranks as a measure of consensus between the two groups. The correlations ranged from .85∼.99 all indicating a high degree of consensus.

• Nutrition Research and Practice
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• v.8 no.6
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• pp.713-718
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• 2014

BACKGROUND/OBJECTIVES: Studies have indicated that university students majoring in nutrition and dietetics or sport sciences may have more obsessions associated with eating attitudes and body shape perception compared to other disciplines i.e. social sciences. Therefore, this study aimed to assess and compare the risk of eating disorders and body shape perception. MATERIALS/METHODS: Data was collected from 773 undergraduate students at the Departments of Nutrition and Dietetics (NDD) (n = 254), Physical Education and Sports (PESD) (n = 263), and Social Sciences (SOC) (n = 256).A socio-demographic and personal information questionnaire, Eating Attitudes Test (EAT-40), Body Shape Questionnaire (BSQ-34), Perceived Figure Rating Scale (FRS) were applied; and body weights and heights were measured. RESULTS: Mean EAT-40 scores showed that, both male and female students of PESD had the highest scores ($7.4{\pm}11.6$) compared with NDD ($14.3{\pm}8.3$) and SOC ($13.0{\pm}6.2$) (P < 0.05). According to EAT-40 classification, high risk in abnormal eating behavior was more in PESD (10.7%) compared to NDD (2.9%) and SOC (0.4%) students (P < 0.05). Students of PESD, who skipped meal, had higher tendency to the risk of eating disorders (P < 0.05). In parallel, body shape perception was found to be marked with higher scores in NDD ($72.0{\pm}28.7$) and PESD ($71.5{\pm}32.8$) compared with SOC ($64.2{\pm}27.5$) students (P < 0.05). Considering BSQ-34 classification, high concern (moderate and marked) for body shape were more in PESD (7.4 %) compared to NDD (5.2%) and SOC (1.9%) students (P < 0.05). The body size judgement via obtained by the FRS scale were generally correlated with BMI. The Body Mass Index levels were in normal range (Mean BMI: $21.9{\pm}2.8kg/m^2$) and generally consistent with FRS data. CONCLUSIONS: Tendency to the abnormal eating behavior and substantial body shape perception were higher in PESD students who have more concern on body shape and were not well-educated about nutrition. In conclusion, substantial concern on physical appearance might affect eating behavior disorders in PESD students.

• Journal of Korean Academy of Nursing
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• v.25 no.1
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• pp.30-44
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• 1995

This study aimed at analyzing the trend of re-search on pain in Korea, suggesting direction future pain research, and contributing to the use of pain interventions in nursing practice. Research studies on pain were selected from journals of medical and nursing schools, the Korean Nurse, the Korean Nurses' Academic Society Journal, the Central Journal of Medicine, the New Medical Journal, and from theses and dissertations, which were conducted between 1970 and 1994. The total number of the studies was 93. These studies were analyzed for 1) time of publication or presentation, 2) thesis for a degree or nondegree, 3) research design, 4) characteristics of subjects used in each study, 5) measurement tool, 6) types of correlated variables, 7) Korean terms for pain 8) types of nursing interventions, and 9) results of studies. The findings of the analysis can be summerized as follows : 1) The number of studies related to pain has increased rapidly since the early 1980's. The number of experimental research studies related to pain has increased chronologically, but the number of survey research studies related to pain was highest from 1981 to 1985, after that it decreased slowly. 2) The subjects in 19 studies were healthy people and, in 73 studies, patients with various illnesses. Thirty two studies were conducted with surgical patients. 3) Sixty one pain research studies were done for a thesis for a degree and 32 were nondegree research studies. 4) As measurement tools for pain, self- report pain scales were used in 54 studies and more than two tools were used in 28 studies. In the experimental studies, the trend was to use more than two tools. And in the nonexperirnental studies, the trend was to use self-report pain scales only. 5) There were 11 correlational studies. In these studies, the trend was to study anxiety, depression and variables such as intravenous infusion as related to pain.6) In the thirty six experimental studies, the effects of 16 types of nursing interventions weretested. Teaching and information, and relaxation technique were the most popular interventions for pain. 7) In eighteen methodological studies, the majority were studies testing the validity and re-liability of Dr. Lee's Korean Pain Rating Questionnaire. The following suggestions are made based on the above findings : 1) The patterns of these studies related to pain in Korea need to be compared with trends in other countries. 2) Meta - analysis should be done to analyze and integrate the results of various studies. 3) This analysis of pain research is needed to identify the present trend of pain research and to suggest the direction of future pain research, so these patterns of studies should be done in 5 to 10 year intervals. 4) More replicated pain research is needed to prove the effect of nursing interventions and more qualitative research on pain is needed to identify indepth the meaning of pain. 5) Pain researchers should make an effort to apply research result in various clinical settings and try to carry out team research with clinical nurses or with other multidiscipinary researchers.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.7 no.2
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• pp.190-202
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• 1996

This study assessed psychiatrically referred 5-to 13-year-old children who presented inattention or hyperactivity as chief complaints. Demographic characteristics, primary diagnosis, and comorbid psychiatric conditions of them were identified, and they were assessed using questionnaires and neuropsychological tests. Primary diagnoses included ADHD, anxiety disorder, mental retardation, depression, oppositional defiant disorder, developmental language disorder and others. functional enuresis, conduct disorder, and developmental language disorder were among the secondarily diagnosed disorders. In patients diagnosed as ADHD, overall comorbidity rate was 55.3%. The disorders that frequently co-occured with ADHD were specific developmental disorder, conduct disorder, oppositional defiant disorder, anxiety disorder and other. ADHD groups with or without comorbidity differed in performance IQ and CPT scores. ADHD group differed from externalizing disorders group in the information subscore of IQ, MFFT, and CPT scores, and differed in teachers rating scales, the uncommunication factor of CBCL, and CPT card error compared with internalizing disorders group. The authors concluded that inattentive or hyperactive children should be assessed using various instruments to differentiate other disorders and to identify possible presence of comorbid conditions.

• The Journal of Korean Medicine
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• v.39 no.3
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• pp.61-72
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• 2018

Objectives: This study conducted a survey research to characterize traffic accident(TA) patients seeking Korean medicine treatment and to analyze the symptom improvement and satisfaction scores. Methods: A survey was conducted in 122 outpatients, who visited OO University Korean Hospital due to TA-associated symptoms from November, 2017 to May, 2018. The questionnaire included information on patient demographic characteristics, accident circumstance details, pain levels, reason for treatment selection, treatment methods, treatment purpose, symptom improvement and satisfaction. All statistical analyses were performed using Windows SPSS version 20.0 Results: The characteristics of traffic accident patients using Korean medical institution were usually accidents that occurred while driving slowly, and minor injuries like a sprain. The most important consideration in choosing Korean medical institution was its past experience. The biggest reason for switching treatment from Western medicine to Korean medicine was for diverse treatments. Satisfaction with Korean medicine was measured between very satisfaction and satisfaction. Patients showed the highest satisfaction with acupuncture, followed by pharmacopuncture and chuna manual therapy. Most treatments were measured between very satisfaction and satisfaction. After treatment, 95.90 percent of the patients said they would recommend Korean treatment. Conclusions: Although this study has limitations as research in survey format, we intended to analyze determining factors for the use of Korean medicine treatment through satisfaction, symptom improvement, and Numeric rating scale (NRS) change.