• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.93-97
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• 2009

Purpose: Recognize circulation work system and fixing work system's merits and demerits that is enforced in operation of PET/CT center in sudden increase recently. Wish to estimate connectivity degree of individual exposure dose and PET/CT working that is managed periodically through this and look for operation efficiency of PET/CT center. Materials and Methods: (1) Find interrelationship of length of service to be individual exposure dose and PET/CT through TLD interpretation. Specially, evaluate on the basis of data of 2.5 years until 2 quarters 2006~2008 year that show patient increase rapidly the latest. (2) Recognize what countermeasure is evaluating problems happened at circulation work system and fixing work system. Results: Patient examination's number was 14,674 items until 2 quarters 2006~2008 year, and the $^{18}F$-FDG average injection amount was 461.5 MBq. 2 people of 10 radiotechnologist did fixing work PET area and GAMMA area each, and 8 people did circulation work of 3 times for 2.5 years. Average exposure dose that PET area and Gamma area's circulation men in service receive was 1.32 mSv, and PET area men in services came out average 0.825 mSv high than Gamma area men in services. Nurse's exposure dose is 0.28 mSv, and next 2 reason is conjectured. One is contact with patient that medicate $^{18}F$-FDG injection, and another is consultation about patient's next time schedule after examination end. Although exposure dose's amount is not much, is expected to consider continuation work possibility by exposure dose in case is a nurse with pregnancy possibility. Also, $^{131}I$-isotope therapy area's radiotechnologist that use capsule appeared by 0.12 mSv and a nuclear medicine doctor appeared by exposure dose that is less of 0.11 mSv. Conclusions: In case do PET/CT center circulation work after a long time, connoted danger that most men in service is consecutiveness deficiency of business and individual exposure dose increase at early 1 month. Specially, way for individual exposure dose's decrease should be considered. Also, need to evaluate abhorrent work form for efficient work system introduction, and enforce circulation and fixing work suggestion suitable shift working. Finally, must make normalized business guide and so on to prevent circulation work people's business efficiency decline.

• Korean Journal of Environmental Agriculture
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• v.30 no.2
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• pp.209-215
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• 2011

BACKGROUND: The paddy ecosystem is periodically disturbed with a relatively consistent cycle in short term. However, in long term aspect, the paddy as habitats of organisms has been affected by the change in farming practices. Accordingly, the composition and their densities of fauna species inhabiting the wet paddy has been changed. The geological distribution of a species is very helpful to understand the past and current status of habitats and biodiversity. METHODS AND RESULTS: We monitored 290 sites of open plain paddy or terraced valley paddy located in 138 cities or counties of South Korea and analyzed examine geological distribution of a taxon of freshwater invertebrates, diving beetles (Dytiscidae) which inhabited the paddy ecosystem. This survey was conducted from 2005 through 2007. The total species of diving beetles found in the paddy were identified to be 15 genus 26 species among the family of Dytiscidae. Among them, 24 species were found in the terraced valleys-in paddy fields, and 19 species were found in the open plain paddy fields. Eleven species of them were rarely found in the paddy. The average body size of the adult diving beetles of each species was between 2.0 and 35.0 mm. Most of the diving beetle species except for 11 species with rare frequency of occurrence were found in almost all sites of the terraced valley paddy fields but three species (Agabus browni, Agabus japnicus, and Ilybius apicalis) were not found in the open plain paddy fields. The species distributed relatively widely over some sites of the open plain paddy fields were Guignotus japonicus, and Rhantus pulverosus. Specifically, Ilybius apicalis was found in a specific region, the east-southern part of Korean peninsula, whereas Coelambus chinensis was found only in valley paddy field of the region where Ilybius apicalis was not found. Overall distribution range of diving beetles in open plain paddy fields was limited to few area than in terraced valley paddy fields. CONCLUSION(s): The differences in the range of distribution of diving beetles between terraced valley paddy fields and open plain paddy fields was thought to be the result of an complex action of physico-chemical environments such as annual water status and the degree of chemical application involving differences in the extent of disturbance of the paddy ecosystem, the connectivity of the paddy to an adjacent biotope, and interrelationships among competitors.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Korean Journal of Environment and Ecology
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• v.31 no.2
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• pp.252-265
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• 2017

This study aims to analyze the landscape ecological characteristics of green spaces within built up area of high density and evaluate the potential applicability of green patches, thereby introducing urban garden for generating green networks in residence areas. To this end, Yeoksam-Dong was selected as the site area since it is classified as both green initiative zone and alienated area of park service in Seoul. First, the current condition of green spaces in Yeoksam-Dong was identified by five categories: Street trees, private garden, public pocket garden, rooftop garden, and park. Then, the landscape index analysis through FRAGSTATS and connectivity assessment via multi-buffer zone analysis were carried out for analyzing the green networks and evaluating the potential value of green space. The results showed that the degree to which green areas in the site were distributed is arranged in the order of street tree, private garden, public pocket garden, park, and rooftop garden. In case of the street trees whose total core area (TCA, $1,618m^2$) is as high as the park's ($1,128m^2$). Private garden has potential for green network in built up area of high density by gardening since the shape of the patches are irregular (ED = 78.1m/ha) and the average distance among the patches is close (ENN=33.9m). Public pocket garden has also potential for gardening according to the result that it was found to be distributed evenly (LPI=5.7%, SHEI=0.9) with exposing external disturbance ($TCA=66m^2$). For the green network, 84% of all the study site is covered by small green network in 50m butter range of connected green area. The effect of green network was expected through gardening in public pocket garden (27%) and street tree (26%). Accordingly, it is encouraged to actively utilize street tree, private gardens, and rooftop gardens and to establish the urban gardens like local-based community gardens in public pocket garden where a variety of activities can be carried out near residential areas. By doing so, green networks can effectively be established in built up area with high density. The results of this study can contribute positively to fostering the creation of various types of urban gardens.

• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.17-35
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• 2021

The purpose of this study is to derive implications for the development of the next home economics curriculum by comparing the 'function' and 'achievement standard' presented in the 14 subjects of the 2015 revised middle school common curriculum with the home economics curriculum. For this, keyword network analysis was conducted, and the results are as follows. First, in the 'function' of the 2015 revised middle school common curriculum, 'analysis, use, and expression' were found to be core function keywords with high Degree Centrality and the Eigenvector Centrality. Second, the functional keywords 'understanding, explanation, expression, analysis, and use' in the 'achievement standard' of the 2015 revised middle school common curriculum appeared with high frequency, and 'practice, problem-solving, search and reasoning' which are related to practical problem-solving ability appeared. It was confirmed that 'appreciation, solution and realization', which have relatively high Eigenvector Centrality, were core functional keywords used in the 'achievement standard'. Third, when the 'function' and 'achievement standard' of the 2015 revised middle school home economics curriculum were matched and compared, 7 out of 15 functions were not used in the statement of 'achievement standard', so the connection between 'function' and 'achievement standard' appeared to be insufficient. In addition, the diversity of functional keyword used in the 'achievement standard' was also found insufficient when compared to the middle school common curriculum. Therefore, this study propose strengthening the connectivity of 'function' and 'achievement standard' in the next home economics curriculum, using keywords such as 'analyze', 'express', 'compare', 'understand', 'interpret', 'explore', 'appreciate', and 'solve'.

• Asia Marketing Journal
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• v.13 no.3
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• pp.103-134
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• 2011

The recent economic recession and price rise reduces excessive consumption as a whole. So companies take more interest in and use discount coupons as a means of sales promotion to reinforce their competitiveness. The combination of Internet and mobile communication technology leads to an explosive increase in the use of mobile Internet service, which promotes commercialization of mobile coupons. Nevertheless, there are absolutely insufficient researches on mobile coupons than those on paper ones. In this context, this study tries to consider intention of accepting and using mobile coupons. The innovated Technology Acceptance Model (TAM) was used to see factors of using mobile coupons considered important by customers. Through the combination of characteristics of mobile coupon service and values obtained from mobile coupons, effects of variables to enhance intention of using mobile coupons were empirically analyzed. In particular, this study suggested importance of psychological as well as economic values of mobile coupons and emphasized good considerations of the psychological aspect, such as shame, stinginess, and reputation sensitivity, in using mobile coupons as an important factor for intention of using the coupons. Another empirical analysis was made of what moderating roles consumers' self-construalplayed in the effects of mobile coupon values perceived by consumers on intention of using coupons. As a result, immediate connectivity and situational provision among characteristics of mobile coupon service were found to affect ease and usability. It was also shown that perceived ease and usability had significant effects on both economic and psychological values, which then had significant effects on intention of using a mobile system. After testing moderating effects of self-construal, the degree of effects of perceived mobile coupon values on intention of using mobile coupons was greater among inter-dependent self-construal users than among independent ones. This study considered various schemes of improving intention to use mobile coupons and provided a foundation to help companies make a strategy for mobile coupons to be activated in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.