• 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.

• 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 Intelligence and Information Systems
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• v.21 no.2
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• pp.191-204
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• 2015

According to the '2013 Performance Assessment Report on the Financial Program' from the National Assembly Budget Office, the unfilled recruitment ratio of Software(SW) Developers in South Korea was 25% in the 2012 fiscal year. Moreover, the unfilled recruitment ratio of highly-qualified SW developers reaches almost 80%. This phenomenon is intensified in small and medium enterprises consisting of less than 300 employees. Young job-seekers in South Korea are increasingly avoiding becoming a SW developer and even the current SW developers want to change careers, which hinders the national development of IT industries. The Korean government has recently realized the problem and implemented policies to foster young SW developers. Due to this effort, it has become easier to find young SW developers at the beginning-level. However, it is still hard to recruit highly-qualified SW developers for many IT companies. This is because in order to become a SW developing expert, having a long term experiences are important. Thus, improving job continuity intentions of current SW developers is more important than fostering new SW developers. Therefore, this study surveyed the job continuity intentions of SW developers and analyzed the factors associated with them. As a method, we carried out a survey from September 2014 to October 2014, which was targeted on 130 SW developers who were working in IT industries in South Korea. We gathered the demographic information and characteristics of the respondents, work environments of a SW industry, and social positions for SW developers. Afterward, a regression analysis and a decision tree method were performed to analyze the data. These two methods are widely used data mining techniques, which have explanation ability and are mutually complementary. We first performed a linear regression method to find the important factors assaociated with a job continuity intension of SW developers. The result showed that an 'expected age' to work as a SW developer were the most significant factor associated with the job continuity intention. We supposed that the major cause of this phenomenon is the structural problem of IT industries in South Korea, which requires SW developers to change the work field from developing area to management as they are promoted. Also, a 'motivation' to become a SW developer and a 'personality (introverted tendency)' of a SW developer are highly importantly factors associated with the job continuity intention. Next, the decision tree method was performed to extract the characteristics of highly motivated developers and the low motivated ones. We used well-known C4.5 algorithm for decision tree analysis. The results showed that 'motivation', 'personality', and 'expected age' were also important factors influencing the job continuity intentions, which was similar to the results of the regression analysis. In addition to that, the 'ability to learn' new technology was a crucial factor for the decision rules of job continuity. In other words, a person with high ability to learn new technology tends to work as a SW developer for a longer period of time. The decision rule also showed that a 'social position' of SW developers and a 'prospect' of SW industry were minor factors influencing job continuity intensions. On the other hand, 'type of an employment (regular position/ non-regular position)' and 'type of company (ordering company/ service providing company)' did not affect the job continuity intension in both methods. In this research, we demonstrated the job continuity intentions of SW developers, who were actually working at IT companies in South Korea, and we analyzed the factors associated with them. These results can be used for human resource management in many IT companies when recruiting or fostering highly-qualified SW experts. It can also help to build SW developer fostering policy and to solve the problem of unfilled recruitment of SW Developers in South Korea.

• Journal of Environmental Health Sciences
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• v.17 no.2
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• pp.78-94
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• 1991

The purpose of this study is to research the actual conditions of industrial accident patients and to produce worker satisfaction and a rational and effective counter measure pain. Direct interviews with 179 cases (in and out patients) were carried out during a three month period from April to July 1990, at six hospitals two general hospitals Sun Lin and Sung Mo in Po Hang, and four general hospitals in Taegu Kyung Pook University Hospital, Dong San Medical Center, Young Nam Medical Center and Catholic Hospital. The results of this study are summarized as follows: 1. Among the 179 cases, 51.6 % were male and 48.4 % were female. The two largest age groups were 30~39, 31.8 % and 20~29, 27.4 %. Among the 179 cases, 51.6% were married, the largest family number was 2 to 3, 41.1% and 4 to 5, 25.6%. Educationally, graduation from high school was the largest group, 46.4% among the patients, followed by middle school and primary school. The largest group income level was from 40~69만원, 45.2%. The largest group of patients who worked over 50 hrs. a week was 52.0%. The largest group of patients who worked less than 1 year was 44.7%, of the patients in work places of less than 100 people, 60.3% were injured and in work places of 100~299 people, 20.1% were injured. In manufacturing, the lagest group injured was 55.3%, the next group was transport, stroage, communication. The largest group of production workers injured was 40.2%. 2. The cause of injury in the largest group was facility problems, 33.5%. The next group was unsafe habits, 30.2% a lack of safety knowledge, 17.9% and insufficient supervision, 12.3%. The 30~39 year age group was head the highest number of injuries, 40.4% work places with more than 10 yeras of work, 44.4% work palces with more than 1000 people, 56.3% and mining accidents, 80.0%. Among these groups the highest cause of injury was due to facility problems. 3. The accident pattern showed machinery injuries 28.5% as the largest group, followed by falls & falling objects 17.3%, fire & electric 15.1%, struke by an object 14.5%, followed by overaction and vehicular accidents. The accident pattern showed 46.4 % among workers over the 50 year age group, workers in the 5~10 year group, 50.0 % places employing more than 1000 workers, 35.3 % : construction 73.7%, and construction workers 57.1%, among these fall & falling objects caused the greatest number of injuries. 4. The largest group of injuries was fractures 54.8%, trauma 14.5%, amputation 11.7%, open wound, and burns. The largest number of fractures occurred in people in the 30~39 year age group, 63.2 % over 10 years of work, 55.6% in work places of 300~400 people, 63.6% construction 63.2% and general workers 57.2 %. 5. The largest group of injuries was upper extremity 45.3%, lower extremity 24.0%, trunk 18.5 % and head or neck 12.2%. Of these groups, upper extremity injuries were the highest in those less 20 years old 75.0%, less than 1 years of work 59.5%, in work places of 500~999 people 60.0%, manufacturing 56.6 % and production workers 55.6%. 6. Periods of injury showed 34 people injured in September, to be the largest followed by October, 32 August, 22 people July, 19 people and the lowest December, 2 people. During the week, Friday had the largest group injured, 35 people followed by Saturday, 26 people and the lowest was Wednesday, 17 people, During the day 1400 hours had the largest group injured, 38 people followed by 800 hours, 31 people. 7. On a basis of 5 as the highest mark, the average, according to worker satisfaction showed facility safety 3.55, work environment 3.47, income 3.44, job 3.21 and treatment 2.98. 8. The correlation between general characteristics and injury showed that age was directly correlated to the duration of work(r=.2591) p<0.01, age was directly correlated to industry (r=2311) p<0.01, and the duration was directly correlated to occupation(r =.4372) p<0.001.

• Journal of Soil and Groundwater Environment
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• v.7 no.4
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• pp.31-39
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• 2002

Pollutants from industry, mining, agriculture, and other sources have contaminated sediments in many surface water bodies. Sediment contamination poses a severe threat to human health and environment because many toxic contaminants that are barely detectable in the water column can accumulate in sediments at much higher levels. The purpose of this study was to make optimal treatment and disposal plan o( sediment for water quality improvement in small-scale resevoir based on an evaluation of degree of contamination. The degree of contamination were investigated for 23 samples of 9 site at different depth of sediment in small-scale J river. Results for analysis of contaminated sediments were observed that copper concentration of 4 samples were higher than the regulation of hazardous waste (3 mg/L) and that of all samples were exceeded soil pollution warning levels for agricultural areas. Lead and mercury concentration of all samples were detected below both regulations. Necessary of sediment dredge was evaluated for organic matter and nutrient through standard levels of Paldang lake and the lower Han river in Korea and Tokyo bay and Yokohama bay in Japan. The degree of contamination for organic matter and nutrient was not serious. Compared standard levels of Japan, America, and Canada for heavy metal, contaminated sediment was concluded as lowest effect level or limit of tolerance level because standard levels of America and Canada was established worst effect of benthic organisms. The optimal treatment method of sediment contained heavy metal was cement-based solidification/stabilization to prevent heavy metal leaching.

• Korean Journal of Environmental Agriculture
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• v.24 no.3
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• pp.215-221
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• 2005

Numerous abandoned or closed mines are present in the steep mountain valleys in Korea due to the depression of the mining industry since the late 1980s. From the mines, enormous amounts of wastes were dumped on the slopes causing sedimentation and acid mine drainage to be discharged directly into streams causing detrimental effects on surrounding environment. Objective of this research was to evaluate the feasibility of the lime cake by-product from the soda ash production (Solvay process) to neutralize the pyrophyllite mine wastes, which have discharged the acid drainage to soil and stream in the watershed. The pH of mine wastes was strongly acidic at pH 3.67 containing over 16% of $Al_2O_3$ and 11% of $Fe_2O_3$. Whereas the lime cake by-product was strongly basic at pH 9.97 due to high contents of CaO, MgO and $CaCl_2$ as major components. Column experiments were conducted to test the neutralizing capacity of the lime cake by-product for the acidic pyrophyllite mine wastes. The column packed with the wastes (control) was treated with the lime cake by-product, calcium carbonate, the dressing soil or combination. The distilled water was eluted statically through the column and the leachate was collected for the chemical analyses. Treatments of the mine wastes with the lime cake by-product (or calcium carbonate) as mixtures increased pH of the leachate from $3.5{\sim}4.0\;to\;7{\sim}8$. Concentrations of Fe and Al in the leachate were also decreased below 1.0 mg $L^{-1}$. A Similar result was observed at the combined treatments of the mine waste, the lime by-product (or calcium carbonate) and the dressing soil. The results indicated that the lime cake by-product could sufficiently neutralize the acid drainage from the pyrophyllite mine wastes without dressing soils.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• The Journal of Korean Physical Therapy
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• v.3 no.1
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• pp.151-174
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• 1991

The purpose of this study is to research the actual conditions of industrial accident patients and to produce worker satisfaction and a rational and effective counter measure plan. Direct interviews with 179 cases (in and out patients) were carried out during a three month period from April to July 1990, at six hospitals : two general hospitals Sun Lin and Sung Mo in Po Hang, and four general hospitals in Taegu : Kyung pooh University Hospital, Dong San Medical Center, Young Nam Medical Center and Catholic Hospital. The results of this study are summarized as fellows : 1. Among the 179 cases, $51.6\%$ were male and $48.4\%$ were female. The two largest age groups were 30-39, $31.8\%$ and 20-29, $27.4\%$. Among the 179 cases, $51.6\%$ were married, the largest family number was 2 to 3, $41.1\%$ and 4 to 5, $25.6\%$. Educationally, graduation from high school was the largest group, $46.4\%$ among ,the patients, followed by middle school and primary school. The largest group income level was from 40-69 만원, $45.2\%$. The largest group of patients who worked over 50 hrs. a week was $52.0\%$. The largest group of patients who worked less than 1 year was $44.7\%$, of the patients in work places of less than 100 people, $60.3\%$ were injured and in work places of 100-299 people, $20.1\%$ were injured. In manufacturing, the largest group injured was $55.3\%$, the next group was transport, storage, communication. The largest group of production workers injured was $40.2\%$. 2. The cause of injury in the largest group was facility problems, $33.5\%$. The next group was unsafe habits, $30.2\%$ ; a lack of safety knowledge, $17.9\%$ ; and insufficient supervision, $12.3\%$. The 30-39 year age group head the highest number of injuries, $40.4\%$ ; work places with more than 10 years of work, $44.4\%$ ; work places with more than 1000 people, $56.3\%$ and mining accidents, $80.0\%$. Among. these groups the highest cause of injury was due to facility problems. 3. The accident pattern showed machinery injuries $28.5\%$ as the largest group, followed by falls & falling objects $17.3\%$, fire & electric $15.1\%$, strucke by an object $14.5\%$, followed by overaction and vehicular accidents. The accident pattern showed $46.4\%$ among workers over the 50 year age group, workers in the 5-10 year group, $50.0\%$ ; places employing more than 1000 workers, $35.3\%$ ; construction $73.7\%$, and construction workers $57.1\%$, among these fall & falling objects caused the greatest number of injuries. 4. The largest group of injuries was fractures $54.8\%$, trauma $14.5\%$, amputation $11.7\%$, open wound, and burns. The largest number of fractures occurred in people in the 30-39 year age group, $63.2\%$ : over 10 years of work, $55.0\%$ ; in work places of 300-490 people, $63.6\%$ ; construction $63.2\%$ and general workers $57.2\%$. 5. The largest group of injuries was upper extremity $45.3\%$, lower extremity $24.0\%$, trunk $18.5\%$ and head or neck $12.2\%$. Of these groups, upper extremity injuries were the highest in those less than 20 years old $75.0\%$, less than 1 year or work $59.5\%$, in work places of 500-999 people $60.0\%$, manufacturing $56.6\%$ and production workers $55.6\%$. 6. Periods of injury showed 34 people injured in September, to be the largest followed by October, 32 ; August, 22 people : July, 19 people and the lowest December, 2 people. During the week, Friday had the largest group injured, 35 people ; followed by Saturday, 26 people and the lowest was Wednesday, 17 people, During the day 1400 hours had the largest group injured, 38 people ; followed by 800 hours, 31 people. 7. On a basis of 5 as the highest mark, the average, according to worker satisfaction showed facility safety 3.55, work environment 3.47, income 3.44, job 3.21 and treatment 2.98. 8. The correlation between general characteristics and injury showed that age was directly correlated to the duration of work (r=2591) p<0.01, age was directly correlated to industry (r=2311) p<0.01, and the duration was directly correlated to occupation (r=4372) p<0.001.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.