• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.421-431
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• 2014

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.985-995
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• 2015

Add-on selling can provide new chances to increase sellers' profits and meet customers' needs. Although prior studies have advocated add-on selling for its business value, there is an argument that add-on selling can cause customer repulsion. Therefore, we need to understand customer purchasing pattern related to add-on selling in order to promote it and to mitigate the customer repulsion. To that end, we applied data mining techniques to the 24,925 transactions of data from an online open market in Korea. We then conducted feature selection to investigate the most influential factors that can explain the characteristics of add-on selling transactions using a classification model. We also identified association rules among add-on selling and promotions. Finally, based on the findings in our experiments, we proposed add-on selling strategies for the target online market.

• Journal of Korean Society of Transportation
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• v.24 no.4 s.90
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• pp.103-113
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• 2006

Market Segmentation for Freight Transportation has been used to know the strategies both efficiency of freight transportation market and attraction of freight volume for carriers. It was so difficult to understand the individual preference of shippers that all shippers could be only homogenized through market segmentation. In Korea. standard Industrial classification has been used for freight market segmentation. This study evaluated another new market segmentation method for manufacturing industry. From the study, we knew that the best relevant market segmentation criterion was annual input-output volume, which showed excellent segmenting ability. Also. the results showed many differences against segmentation results according to standard industrial classification. This study had a meaning as a new trial which segmented freight transportation market using company size and shipment characteristic data.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.627-636
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• 2022

Market convergence plays an increasingly important role in sustaining competitiveness and providing impetus for the new product development. However, existing research focused mostly on the analysis of convergence at technology level. This study examines the phenomenon of market convergence based on the start-up data. Similar to the analysis of technology convergence, this study adopts the concept of co-classification analysis for constructing the co-occurrence matrix and the corresponding network. In this context, network centrality measures were calculated to assess the influence of individual market segments. Based on three metrics "growth", "persistence" and "novelty", the market convergence dynamics were explored and promising interactions between two distinct market segments were highlighted. The findings suggest that both segments "AI" and "blockchain" are acting as a driver that fosters market convergence in the startup landscape. The analysis results can provide valuable information for the R&D managers and policy makers in the design of targeted policies and programs, which can promote market convergence and interdisciplinary knowledge transfer.

• Journal of Korean Society for Quality Management
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• v.32 no.3
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• pp.198-215
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• 2004

Technology competitiveness evaluates environmental and engineered technology and process at both the scientific and market levels. There are increasing concerns to measure the effects of the technology variables on the potential market feasibility levels. However, there are very little empirical analysis studies on that issue. This study investigates the impacts of technology variables on the levels of market feasibility based on 230 data obtained from Korea Technology Transfer Center. As various statistical analysis, the canonical discriminant model, logit discriminant model and classification model were used and their results were compared. This study results showed that major technology variables had very significant relations to discriminate high and low categories of market feasibility. Finally, this study will help building management strategies to level up the potential market performance and also help financial Institutions to decide funds needed for small-sized technology firms.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.609-616
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• 2016

This paper is proposed a customized market analysis method using SVM and collaborative filtering. The proposed customized market analysis scheme is consists of DC(Data Classification) module, ICF(Improved Collaborative Filtering) module, and CMA(Customized Market Analysis) module. DC module classifies the characteristics of on-line and off-line shopping mall and traditional markets into price, quality, and quantity using SVM. ICF module calculates the similarity by adding age weight and job weight, and generates network using the similarity of purchased item each users, and makes a recommendation list of neighbor nodes. And CMA module provides the result of customized market analysis using the data classification result of DC module and the recommendation list of ICF module. As a result of comparing the proposed customized recommendation list with the existing user based recommendation list, the case of recommendation list using the existing collaborative filtering scheme, precision is 0.53, recall is 0.56, and F-measure is 0.57. But the case of proposed customized recommendation list, precision is 0.78, recall is 0.85, and F-measure is 0.81. That is, the proposed customized recommendation list shows more precision.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.192-193
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• 2014

Due to the decreasing domestic construction market since 2007, Korean construction companies are expanding overseas market. As a result, the international market share by Korea has been continuously increased and achieved 65.2 billion dollars in 2013. Despite of such visible results, profitability concerns are constantly arising. It is pointed out that the low-priced bid competition between Korean construction companies and various unpredictable risks are the most crucial factors which aggravate the profitability in the overseas projects. From this point of view, predicting the risks in advance and controling them could be the most important tasks to improve the profitability. This research proposed 202 risk factors with a hierarchy and relational database schema for power plant construction, which is based on the 24 risk classifications in previous research (Kim & Jung 2013). Proposed risk classification and relational database schema could be utilized as the basic data in risk management system.