• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.

• Korean Journal of Forensic Psychology
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• v.11 no.2
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• pp.135-153
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• 2020

Identifiable victim effect refers to the tendency of greater sympathy and helping behavior to identifiable victims than to abstract, unidentifiable ones. This research tested whether this tendency also affects third-party's punishment and compensation judgments in jury context for public's legal judgments. In addition, through the Identifiable victim effect in such legal judgment, we intended to explain the effect of 'the bill named for victim', putting the victim's real name and identity at the forefront, which is aimed at strengthening the punishment of related crimes by gaining public attention and support. To do so, we conducted experiments with hypothetical traffic accident scenarios that controlled legal components while manipulating victim's identifying information. In experiment 1, each participant read a scenario of an anonymous victim (unidentifiable condition) or a nonanonymous victim that included personal information such as name and age (identifiable condition) and made judgments on the degree of punishment and compensation. The results showed no effect of identifiability on third-party's punishment and compensation judgments, but moderation effect of BJW was obtained in the identifiable condition. That is, those with higher BJW showed greater tendency of punishment and compensation for identifiable victims. In Experiment 2, we compared an anonymous victim (unidentifiable condition) against a well-conducted victim (positive condition) and ill-conducted victim (negative condition) to test the effects of victim's characteristics on punishment for offender and compensation for victims. The results showed lower compensation for an ill-conducted victim than for an anonymous one. In addition, across all conditions except for negative condition, participants made punishment and compensation judgments higher than the average judicial precedents of 10-point presented in the rating scale. This research showed that victim's characteristics other than legal components affects third-party's legal decision making. Furthermore, we interpreted third-party's tendency to impose higher punishment and compensation with effect of 'the bill named for victim' and proposed social and legal discussion for and future research.

• Korean Journal of Heritage: History & Science
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• v.56 no.2
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• pp.246-262
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• 2023

This study analyzed policy implementation factors focusing on how Donuimun, a demolished cultural heritage, was digitally restored and the policy implementation process of Donuimun Gate restoration. Through this, the characteristics of the implementation process of the digital Donuimun Gate restoration policy promoted by public-private multilateral collaboration were examined and implications were sought for how institutions with different interests solved problems and collaborated in the implementation process. The research method was focused on policy implementation factors including policy executive factors, policy content factors, policy resource factors, and policy environment factors, and the process was analyzed for each detailed component. Along with literature analysis, in-depth interviews were conducted with participants in policy implementation. As a result of the study, first, it was found in the policy executive factor that the quick decision-making leadership of the policy manager and the flexible attitude of the person in charge of the government agency had a positive effect on preventing conflicts between different interest groups. Second, in terms of policy content, establishing a common goal that everyone can accept and moving forward consistently gave trust and created synergy. Third, in the policy implementation resource factor, the importance of the budget was emphasized. Finally, as an environmental factor for policy implementation, the opening of 5G mobile communication for the first time along with the emergence of the Fourth Industrial Revolution at the time of policy implementation acted as a timely factor. The digital Donuimun Gate was the first case of restoring a lost cultural heritage with AR and VR, and received attention and support from the mass media and the public. This also shows that digital restoration can be a model case that can be a solution without conflicts with local residents where cultural heritages are located or conflicts between stakeholders in the preservation and restoration of real objects.

• Asia Marketing Journal
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• v.13 no.3
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• pp.209-231
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• 2011

Internet is having strong impact on the consumer's decision making process. Information search has been done actively through internet today. The online reviews can be crucial information cue to evaluate the alternarive products. The online WOM(Word-Of-Mouth) effect depends on the characteristics of information sender, receiver, and WOM. This study is to examine the influence of the online word of mouth on the consumer purchase intention and the moderating role of product involvement, consumer regulatory focus and self-efficacy. Positive customer reviews on the products influence the purchase intention positively and negative customer reviews influence it negatively. Moderating role of involvement in the causal relation between the valence of online reviews and purchase intention is tested. In case of positive WOM, it is predicted that purchase intention for high involvement products is higher than that of low involvement. In case of negative WOM, purchase intention for high involvement product is lower than that of low involvement product. And this study invetigate the moderating role of regulatory focus. In case of positive WOM, it is predicted that promotion focus oriented consumers have higher purchase intention than prevention focus oriented consumers. In case of negative WOM, prediction is that prevention focus oriented consumers have lower purchase intention than promotion focus oriented consumers. Then we examine the moderating role of self efficacy in the causal relation between the valence of online reviews and purchase intention. In case of positive WOM, it is predicted that consumers with low self efficacy have higher purchase intention than consumers with high self efficacy. In case of negative WOM, it is predicted that consumers with low self efficacy have lower purchase intention than consumers with high self efficacy. Emprical results support our prediction and four hypotheses derived from our conceptual framework are all accepted. This study suggest that the level of product involvement, consumer regulatory focus and the level of self-efficacy influence the consumer responses of the valence of online reviews. Therefore marketers need to manage online reviews based on the level of product involvement, regulatory focus orientation and the level of self-efficacy of target consumers.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.775-784
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• 2023

With the increasing trend of extreme rainfall that exceeds the design frequency of man-made structures due to extreme weather, it is necessary to review the safety of agricultural reservoirs designed in the past. However, there are no local government-managed reservoirs (13,685) that can be discharged in an emergency, except for reservoirs over a certain size under the jurisdiction of the Korea Rural Affairs Corporation. In this case, it is important to quickly deploy a mobile siphon to the site for preliminary discharge, and this study evaluated the applicability of a mobile siphon with a diameter of 200 mm, a minimum water level difference of 6 m, 420 (m²/h), and 10,000 (m²/day), which can perform both preliminary and emergency discharge functions, to the Yugum Reservoir in Gyeongju City. The test bed, Yugum Reservoir, is a facility that was completed in 1945 and has been in use for about 78 years. According to the hydrological stability analysis, the lowest height of the current dam crest section is 27.15 (EL.m), which is 0.29m lower than the reviewed flood level of 27.44 (EL.m), indicating that there is a possibility of lunar flow through the embankment, and the headroom is insufficient by 1.72 m, so it was reviewed as not securing hydrological safety. The water level-volume curve was arbitrarily derived because it was difficult to clearly establish the water level-flow relationship curve of the reservoir since the water level-flow measurement was not carried out regularly, and based on the derived curve, the algorithm for operating small and medium-sized old reservoirs was developed to consider the pre-discharge time, the amount of spillway discharge, and to predict the reservoir lunar flow time according to the flood volume by frequency, thereby securing evacuation time in advance and reducing the risk of collapse. Based on one row of 200 mm diameter mobile siphons, the optimal pre-discharge time to secure evacuation time (about 1 hour) while maintaining 80% of the upper limit water level (about 30,000 m²) during a 30-year flood was analyzed to be 12 hours earlier. If the pre-discharge technology utilizing siphons for small and medium-sized old reservoirs and the algorithm for reservoir operation are implemented in advance in case of abnormal weather and the decision-making of managers is supported, it is possible to secure the safety of residents in the risk area of reservoir collapse, resolve the anxiety of residents through the establishment of a support system for evacuating residents, and reduce risk factors by providing risk avoidance measures in the event of a reservoir risk situation.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.15-31
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• 2022

This study was conducted to suggest a start-up policy that includes start-up education and support for active seniors with various careers who try to change their careers before and after retirement. From this point of view, this study divided the factors affecting the entrepreneurial will of active seniors into entrepreneurship organizational and functional competency and identified the effect of these competencies on entrepreneurial efficacy and entrepreneurial intention. In the proposed model, start-up competency is divided into organizational competency (leadership, creativity problem-solving, communication, decision-making) and functional competency (management strategy, marketing, business plan). And this study examined the mediating role of entrepreneurial efficacy in the relationship between entrepreneurial competency factors and entrepreneurial intention. Meanwhile, PLS-POS analysis was performed to uncover the heterogeneity and pattern in the proposed structural model. The survey was conducted with the help of an online survey company from November 27 to December 15, 2020 for the active senior age group from 40 to under 65 years old. Data were collected from a total of 433 panelists and analyzed using SPSS 22.0 and SmartPLS 3.3.7 programs. The findings are as follows. First, the finding shows that the entrepreneurial organizational and functional competencies of active seniors had significant positive(+) effects on entrepreneurial efficacy. Second, the result shows that entrepreneurial organizational and functional competencies of active seniors had significant positive(+) effects on entrepreneurial intention. Third, the findings show that entrepreneurship efficacy had a significantly positive(+) effect on entrepreneurial intention. The findings of PLS-POS show that entrepreneurship education needs to be carried out by identifying the needs that require entrepreneurial organizational and functional competency when training for entrepreneurship competency. In summary, the findings of the current study are to determine what the competency factors are for the government (local government) to increase the policy direction necessary for establishing and implementing entrepreneurship education and training programs to develop policies to enhance the economic activity participation rate of active seniors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.1-30
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• 2024

The international community adopts the SSP (Shared Socioeconomic Pathways) scenario as a new greenhouse gas emission pathway. As part of efforts to reflect these international trends and support for climate change adaptation measure in the agricultural sector, the National Institute of Agricultural Sciences (NAS) produced high-resolution (1 km) climate change scenarios for the Korean Peninsula based on SSP scenarios, certified as a "National Climate Change Standard Scenario" in 2022. This paper introduces SSP climate change scenario of the NAS and shows the results of the climate change projections. In order to produce future climate change scenarios, global climate data produced from 18 GCM models participating in CMIP6 were collected for the past (1985-2014) and future (2015-2100) periods, and were statistically downscaled for the Korean Peninsula using the digital climate maps with 1km resolution and the SQM method. In the end of the 21^st century (2071-2100), the average annual maximum/minimum temperature of the Korean Peninsula is projected to increase by 2.6~6.1℃/2.5~6.3℃ and annual precipitation by 21.5~38.7% depending on scenarios. The increases in temperature and precipitation under the low-carbon scenario were smaller than those under high-carbon scenario. It is projected that the average wind speed and solar radiation over the analysis region will not change significantly in the end of the 21st century compared to the present. This data is expected to contribute to understanding future uncertainties due to climate change and contributing to rational decision-making for climate change adaptation.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.