• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.117-129
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• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of Soil and Groundwater Environment
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• v.11 no.3
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• pp.31-42
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• 2006

Drought indices, such as PDSI (palmer Drought Severity Index), SWSI (Surface Water Supply Index) and SPI (Standardized Precipitation Index), have been developed to assess and forecast an intensity of drought. To find the applicability of groundwater level data to a drought assessment, a correlation analysis between SPI and groundwater levels was conducted for each time series at a drought season in 2001. The comparative results between SPI and groundwater levels of shallow wells of three national groundwater monitoring stations, Chungju Gageum, Yangpyung Gaegun, and Yeongju Munjeong, show that these two factors are highly correlated. In case of SPI with a duration of 1 month, cross-correlation coefficients between two factors are 0.843 at Chungju Gageum, 0.825 at Yangpyung Gaegun, and 0.737 at Yeongju Munjeong. The time lag between peak values of two factors is nearly zero in case of SPI with a duration of 1 month, which means that groundwater level fluctuation is similar to SPI values. Moreover, in case of SPI with a duration of 3 month, it is found that groundwater level can be a leading indicator to predict the SPI values I week later. Some of the national groundwater monitoring stations can be designated as DIW (Drought Index Well) based on the detailed survey of site characteristics and also new DIWs need to be drilled to assess and forecast the drought in this country.

• Journal of Soil and Groundwater Environment
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• v.11 no.3
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• pp.43-51
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• 2006

Generally abandoned mine soils have serious problems for introducing vegetation such as nutrient deficiency, poor physical properties, and phytotoxicity due to high levels of heavy metals. It is required to improve soil amenity for revegetation. One of its strategies is using organic materials such as compost manure and sludge. The pot experiments was conducted to evaluate the effects of pig manure and municipal sewage sludge on revegetation of mining area soil surface with Artemisia princeps and Zoysia japonica. Application rate of pig manure and municipal sewage sludge was $75{\sim}225$ Mg/ha and $150{\sim}450$ Mg/ha, respectively. The results showed that the application of manure and sludge increased organic matter about two-fold and total nitrogen contents about five-fold of mine soil and improved the growth of plants in all treatments compared to the control. The result of plant tissue analysis showed that both plants accumulate Cd, Cu and Zn in root tissue rather than shoot tissues. Increased sludge application reduced Zn accumulation in both plant tissue. Sequential extraction results indicated that addition of soil amendment induced increment of organically bound fractions of Cu and Zn. Organically bound fraction of Zn was significantly increased from 7.84% to 13.58% in Artemisia princeps planted soil and from 7.84% to 14.16% in Zoysia japonica planted soil, thereby bioavailability of heavy metals was reduced. The results suggested that application of organic materials to mine soil can reduce phytotoxicity of heavy metals and be helpful in introducing successful revegetation.

• Land and Housing Review
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• v.2 no.4
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• pp.559-568
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• 2011

This study aims at listing up those manufacturing activities sensitive to regional characteristics by analyzing locational hierarchy designed on the urban rank-size rule. This locational hierarchy by manufacturing activities is expected to provide a ground for the proper supply of an industrial complex. The analysis of the locational hierarchy by manufacturing activities can work as a method of observing the characteristics of the distribution of location for each economic activity by analyzing the trend in the change of manufacturing location. Consequently, it can be used to determine the appropriate manufacturing activities for the industrial complex of a particular region. Here, the locational hierarchy is analyzed depending on the base of the basic local government such as Gun(district level) and Si(city level), and manufacturing activities are categorized by Korea Standard Industry Code. Those activities demonstrating growth pattern are Manufacture of Electronic Equipment(KSIC 26), Manufacture of Medical Precision Optical Instruments Watch(KSIC 27), Manufacture of Motor Vehicles (KSIC 30, 31), etc. With proper infrastructures, these activities can be located everywhere. Those sectors on the decline pattern in the locational hierarchy can be summarized as Manufacture of Tobacco Products(KSIC 12), Manufacture of wearing apparel Fur Articles(KSIC 14), etc. Those sectors scattered widely in the locational hierarchy are Manufacture of Food Products(KSIC 10), Manufacture of Coke Petroleum Products(KSIC 19), Manufacture of Chemical Products(KSIC 20), Manufacture of Electronic Equipment(KSIC 26). These particular manufacturing activities can be operated in those regions in a sufficient supply of unskilled workers regardless of proper infrastructures. Those activities that have a tendency to reconcentrate on larger cities are Manufacture of Textiles(KSIC 13), Manufacture of Wearing Apparel Clothing Fur Articles(KSIC 14), Manufacture of Other Transport Equiptmen(KSIC 31). In most cases, these sectors tend to favor their existing agglomerated areas and concentrate around large cities. Therefore, it is inefficient to promote these sectors in small or medium-sized cities or underdeveloped regions. The establishment of developmental strategies of an industrial complex can gain greater competitiveness by observing such characteristics of the locational hierarchy.

• Korean Journal of Soil Science and Fertilizer
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• v.3 no.1
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• pp.1-10
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• 1970

A pot experiment of paddy rice was carried out primarily to find out the effect of potash in relation to the application of wollastonite on paddy rice. The result which showed increased paddy rice yield has been evaluated from the factors of yield, yield components, soil and plant analysis data at different stages of plant growth and the summary of review is described as follows : 1. Effect of potash is likely to be greater in the presence of wollastonite and a larger quantity of wollastonite application would require correspondingly larger quantities of potash. 2. Application of potash and wollastonite resulted increased paddy rice yield may be largely due to increased contents of potassium, calcium and silica in the soil and increased concentration of $K_2O$, CaO and $SiO_2$, $SiO_2/N$ ratio and $K_2O/N$ ratio in rice straw. 3. The maximum paddy rice yield, at less than 300kg/10a application of wollastonite was obtained by applying 8kg of $K_2O$ per 10 are while 600kg/10a application of wollastonite gave the maximum yield by applying 16kg of $K_2O$ application per 10 are. 4. Application of wollastonite decreased the number of ears per hill and number of grains per ear. While ripening percentage and 1,000 grain weight tended to increase with wollastonite. The increased contents of CaO and $SiO_2$ in straw due to wollastonite application showed a negative correlation with the number of ears per hill and the number of grains per ear but a positive correlation was shown with ripening percentage and 1,000 grains weight. 5. Potash application appeared to be increasing the number of ears per hill and the number of grains per ear. There was a positive correlation exist between the higher content of $K_2O$ in rice straw and the number of grains per ear 6. Amount of nitrogen absorbed tended to decrease with increase in quantities of potash and wollastonite but the ratio of $SiO_2/N$ was increased by further application of potash and wollastonite. 7. $K_2O/N$ ratio was increased with incrementing in quantities of potash application but in the absence of potash, $K_2O/N$ ratio was decreased as to increasing wollastonite application.

• Korean Journal of Soil Science and Fertilizer
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• v.27 no.1
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• pp.27-32
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• 1994

These studies were Conducted to survey cultivation methods, and to analyze soil chemical properties for stable production of Aralia continentalis K., a promising medicinal crop, in the main producting districts. Aralia continentalis K. was cultivated with planting budstocks in distances of $90cm{\times}60cm$ for 3~4 years in the same field, and application rates were N 10~31 kg/10a and P, K 8~17/10a using inorganic fertilizer, that is compound fertilizer(21-17-17) for basal dressings and urea for topdressings. Most of all, the soil surveyed was coarse loamy class which was well drained and soil depth is 50~150cm in the valleys. Soil pH was low, and content of organic matters and av. $P_2O_5$ was abundant but that of exchangeable cation such as Ca, Mg, K was deficient. The relationship between growth characteristics and weight of fresh root was positive correlation in the order of No. of root, stem width, No. of node and branch, plant height and root width. On the path coefficiant analysis, the relationship between content of soil K, Ca and root yield was more apparent than other chemical properties.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.5
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• pp.323-332
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• 2003

This study was carried out to provide information for the present status of soil pollution near abandoned old-zinc mining area through analysis of bound form and 0.1 N-HCl extractable concentrations of heavy metals in soils and plants. Feasibility of endemic plants for phytoremediation was evaluated by the investigation of vegetation in soils. Cd contents of the selected samples near old-zinc mining soils ranged from 0.2 to $42mg\;kg^{-1}$. Nonagricultural soils near the mining area contained great amounts of Zn, Pb, Cd, and Cu than the paddy and upland soils. Some Korean wild plants, Artemisia princeps, Artemisia montana, Erigeron canadensis, and Pueraria thunbergiana, were found to grow vigorously in the studied area. Among them, Artemisia princeps was selected as a possible phytoremediator for cleaning heavy metal contaminated soils. Artemisia princeps contained about 43 and $52mg\;kg^{-1}$ of Cd in their root and shoot as dry weight, respectively. Average contents of Cd in the rhizosphere soil, $15.68mg\;kg^{-1}$, was slightly higher than the soil-root interface soils, $14.1mg\;kg^{-1}$. Sequential extraction of Cd contaminated soils showed that average $2.4mg\;kg^{-1}$ (about 7%) of cadmium existed as exchangeable form and the average amounts increased as follows : adsorbed < organically bound < exchangeable << oxide carbonate << sulfide residual fractions. Amendment of organic by-product fertilizer in metal-contaminated soils promoted the growth of roots significantly as compared with the other treatments containing chemical fertilizer.

• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.45-73
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• 1995

The current paper presents a system dynamics model which can generate the land use anq transportation system performance simultaneously is proposed. The model system consists of 7 submodels (population, migration of population, household, job growth-employment-land availability, housing development, travel demand, and traffic congestion level), and each of them is designed based on the causality functions and feedback loop structure between a large number of physical, socio-economic, and policy variables. The important advantages of the system dynamics model are as follows. First, the model can address the complex interactions between land use and transportation system performance dynamically. Therefore, it can be an effective tool for evaluating the time-by-time effect of a policy over time horizons. Secondly, the system dynamics model is not relied on the assumption of equilibrium state of urban systems as in conventional models since it determines the state of model components directly through dynamic system simulation. Thirdly, the system dynamics model is very flexible in reflecting new features, such as a policy, a new phenomenon which has not existed in the past, a special event, or a useful concept from other methodology, since it consists of a lots of separated equations. In Chapter I, II, and III, overall approach and structure of the model system are discussed with causal-loop diagrams and major equations. In Chapter V _, the performance of the developed model is applied to the analysis of the impact of highway capacity expansion on land use for the area of Montgomery County, MD. The year-by-year impacts of highway capacity expansion on congestion level and land use are analyzed with some possible scenarios for the highway capacity expansion. This is a first comprehensive attempt to use dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions. The model structure is not very elaborate mainly due to the problem of the availability of behavioral data, but the model performance results indicate that the proposed approach can be a promising one in dealing comprehensively with complicated urban land use/transportation system.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.