• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Journal of Agricultural Extension & Community Development
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• v.21 no.4
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• pp.967-1006
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• 2014

This study attempted to provide policy recommendations in promoting new cooperatives established in agriculture based on the 2012 Cooperative Act. A questionnaire survey was conducted with 195 newly established cooperatives as the policy target of this study. The new cooperatives were classified as three kinds namely as 'Business' Cooperatives', 'Consumers' Cooperatives', 'Social Cooperatives' based on their member attributes and objectives. Interesting to note that, all of these new cooperatives born by the new Act has taken the marketing business as their main stream business. Among the three types, 'Business Cooperatives' are ranked the highest amount of capital shares per person in average, having about 30 members in size. In categorization, 'Business Cooperatives' include farmer cooperatives as majority and employee cooperatives. They are usually involved in both production and marketing and even in processing activities, and have tried to secure their business performance by e-commerce and stable business contracts. Their diverse activities are highly associated with their local community. Consumers' Cooperatives include consumer cooperatives and stakeholder cooperatives in achieving welfare of members. This type has lower share in capital but has over 30 members in a cooperative, taking marketing (distribution) business as main and often take advantage of their social network and physical store. Regional relationships are less than producer cooperatives. 'Social Cooperatives' are established by public interest and have around 10 members and lowest per capital. their business and community activity is similar to the consumer cooperatives. This study recommends the needs of designing suitable business models by these three types of cooperatives in the future, while appropriating their membership size for their tangible business operations. The government policy direction should aim to develop their new business opportunities and its management stabilization, especially in conjunction with the existing agricultural cooperatives (Nonghyup). It must be rather than to provide simply policy supports for establishment. An in-depth study is recommended in this regard.

• Asia pacific journal of information systems
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• v.19 no.1
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• pp.147-165
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• 2009

Virtual community(VC) will increasingly be organized as commercial enterprises, with the objective of earning an attractive financial return by providing members with valuable resources and environment. For example, Cyworld.com in Korea uses several community services to enable customers of Cyworld to take control of their own value as potential purchasers of products and services. Although initial adoption is important for online network service success, it does not necessarily result in the desired managerial performance unless the initial usage is continuously related to the continuous usage and purchase. Particularly, the customer who receives relevant online services and is well equipped with online network services, will trust the online service provider and perceive less risk and experience more activities such as continuous usage and purchase. Thus, how to promote continued online service usage or, alternatively, how to prevent discontinuance is a critical issue for VC service providers to consider. By aggregating a wide range of information and online environments for customers and providing trust to its members, the service providers of virtual communities help to reduce the perceived risk of continuous usage and purchase. Drill down, online service managers realize that achieving strong and sustained customers who continuously use online service and purchase on it is crucial. Therefore, the research into this online service continuance will identify the relationship between the initial usage and the continuous usage and purchase. The research of continuous usage or post adoption has recently emerged as an important issue in the IS literature. Individuals' information systems(IS) continuous usage decisions are congruent with consumers' repeat purchase decisions. The TAM(Technology Acceptance Model) paradigm has been strongly confirmed across a wide range from product purchase on EC to online service usage contexts. The analysis of IS usage based on TAM has proven to be successful across almost online service contexts. However, most of previous studies have focused on only an area (i.e., VC or EC). Just little research has tried to analyze the relationship between VC and EC. The effect of some factors on user intention, captured through several theories such as TAM, has been demonstrated. Yet, few studies have explored the salient relationships of VC users' EC acceptance. To fill this gap between VC and EC research, this paper attempts to develop a research model that extends the TAM perspective in view of the additional contributions of trust in the service provider and trust in members on some factors that affect EC and VC adoption. In this extension, we applied the TAM-to-TAM(T2T) model, and analyzed the transfer effect of trust between these two TAMs. The research model was empirically tested on the context of a social network service. The model was to extend TAM with the trust concept for the virtual community environment from the perspective of tasks. By building an extended model of TAM and examining the relationships between trust and the existing variables of TAM, it is aimed to explain a user's continuous intention to use VC and purchase on EC. The unit of analysis in this paper is an individual user of a virtual community. The population of interest is the individual with the experiences in virtual community. The data for this paper was made available via a Web survey of VC users. In total, 281 cases were gathered for about one week, but there were some missing values in the sample and there were some inappropriate cases. Thus, only 248 cases were finally analyzed. We chose the structural equation analysis to test the hypotheses and it is better suited for explaining complex relationships than the other methods. In this test, AMOS was used to test the Structural Equation Model (SEM). Noticeable results have been found in the T2T model regarding the factors affecting the intention to use of virtual community and loyalty. Our result showed that trust transfer plays a key role in forming the two adoption beliefs. Overall, this study preliminarily confirms the salience of trust transfer in online service.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.2
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• pp.107-117
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• 2004

If doubtlessly contributes much to agriculture and rural development. The roles can be summarized as; 1. to activate rural areas and to provide more comfortable and safe rural life with equivalent services to those in urban areas, facilitating distance education, tole-medicine, remote public services, remote entertainment etc. 2. To initiate new agricultural and rural business such as e-commerce, real estate business for satellite officies, rural tourism and virtual corporation of small-scale farms. 3. To support policy-making and evaluation on optimal farm production, disaster management, effective agro-environmental resource management etc., providing tools such as GIS. 4. To improve farm management and farming technologies by efficient farm management, risk management, effective information or knowledge transfer etc., realizing competitive and sustainable farming with safe products. 5. To provide systems and tools to secure food traceability and reliability that has been an emerging issue concerning farm products since serious contamination such as BSE and chicken flu was detected. 6. To take an important and key role for industrialization of farming or lam business enterprise, combining the above roles.

• Journal of the Korean Society of Marine Environment & Safety
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• v.16 no.2
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• pp.141-151
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• 2010

Harmful algal blooms (HABs), commonly known as red tides, are aquatic phenomena caused by the rapid growth and accumulation of certain microalgae, which can lead to marked discoloration of surface waters, and severe impacts on public health, commerce, and the environment. In South Korea, the red tides have been a serious and recurrent problem, especially along the south coast. Plenty of yellow loess was used to control an outbreak of the red tides for 15 years from 1996 until now. Yellow loess was almost sprayed in the vicinity of a large fish farming industry. In this research, the distribution characteristics and density distribution of zooplankton were investigated in autumn (Oct. 2008) and spring (Apr. 2009) using volume backscattering strength (SV) calculated by the zooplankton collected with north pacific standard (NORPAC) net and the echo intensity measured with ADCP at stations on the study area in the spraying ocean of yellow loess (SOYL), and the non-spraying ocean of yellow ocean (NOYL) by the red tide generating every year. The species number and the individuals per unit volume of the zooplankton collected in NOYL was high and it which was collected in SOYL was low. As a result of comparing the volume backscattering strength ($SV_c$) calculated by species and length of the zooplankton collected with NORPAC net with the volume backscattering strength ($SV_m$) calculated by the echo intensity measured with ADCP at stations on the study area, although $SV_c$ and $SV_m$ of NOYL were generally in agreement, $SV_m$ of SOYL was higher than $SV_c$ 4.3dB, i.e. ADCP is greatly influenced by suspended solid in SOYL. The horizontal distribution map of $SV_m$ at the study area in autumn (Oct. 2008) and spring (Apr. 2009) was drawn. $SV_m$ of SOYL is higher than NOYL and autumn is higher than spring. $SV_m$ can suppress the overestimate or underestimate of $SV_c$.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.195-209
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• 2021

According to the 2019-2020 social media usage survey conducted by the Seoul e-commerce center, 5 out of 10 consumers have experienced shopping through social media. The cost of traditional advertising media has been reduced and advertising spending on social media has risen by 74%, indicating that social media is becoming a more important marketing element. While the number of users of social media has increased and corporate marketing activities have increased accordingly, research has been conducted in various aspects of marketing such as user motivation for social media, satisfaction, and purchase intention. There was no subdivided study on the differences in the social media usage frequency of consumers in actual purchasing behavior. This study attempted to identify differences in consumer characteristics by cluster in the agrifood purchase situation by grouping them by type according to the frequency of use of social media for consumers who purchase agri-food online. Product involvement, product need, and online purchase channel Consumer characteristics such as demographic distribution, perceived risk, and eating and lifestyle in each cluster were checked for the three agrifood purchase situations including choice, and types for each cluster were presented. To this end, questionnaire data on the frequency of social media use and online agrifood purchase behavior were collected from 245 consumers, and the validity of the measurement variables was secured through factor analysis and reliability analysis. As a result of cluster analysis according to the frequency of social media use, it was divided into three clusters. The first cluster was a group that mainly used open social media, and the second cluster was a group that used both open and closed social media and online shopping malls; The third cluster was a group with low online media usage overall, and the characteristics of each cluster appeared. Through regression analysis, the effect on product involvement, product need, and purchase channel selection when purchasing agri-food online through each of the three clusters was confirmed through regression analysis. As a result of the regression analysis, the characteristic of cluster 1 in the situation of purchasing agri-food online is a male in his 30s living in a rural area who has no reluctance to purchase agri-food on social media or online shopping malls. The characteristics of cluster 2 are mainly consumers who are interested in purchasing health food, and the consumer characteristics are represented. In the case of cluster 3, when purchasing products online, they purchase after considering quality and price a lot, and the consumer characteristics are represented as people who are more confident in purchasing offline than online. Through this study, it is judged that by identifying the differences in consumer characteristics that appear in the agri-food purchase situation according to the frequency of social media use, it can be helpful in strategic judgments in marketing practice on social media customer targeting and customer segmentation.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.