• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.1-19
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• 2010

In offline environment, unlike traditional advertising model through TV, newspaper, and radio, online advertising model draws instantaneous responses from potential consumers and it is convenient to assess. This kind of characteristics of Internet advertising model has driven the growth of advertising model among various Internet business models. There are, conventionally classified, CPM (Cost Per Mile), CPC (Cost Per Click), and CPS (Cost Per Sales) models as Internet advertising expenditure model. These can be examined in manners regarding risks that stakeholders should stand and degree of responsibility. CPM model that is based on number of advertisement exposure is mechanically exposed to users but not actually recognized by users resulting in risk of wasted expenditure by advertisers without any advertising effect. While on aspect of media, CPS model that is based on conversion action is the most risky model because of the conversion action such as product purchase is determined by capability of advertisers not that of media. In this regard, while there are issue of CPM and CPS models disadvantageously affecting only one side of Internet advertising business model value network, CPC model has been evaluated as reasonable both to advertisers and media, and occupied the largest segment of Internet advertising market. However, CPC model also can cause fraudulent behavior such as click fraud because of the competition or dishonest amount of advertising expenditure. On the user aspect, unintentionally accessed advertisements can lead to more inappropriate expenditure from advertisers. In this paper, we suggest "CPCD"(Cost Per Coupon Download) model. This goes beyond simple clicking of advertisements and advertising expenditure is exerted when users download a coupon from advertisers, which is a concept in between CPC and CPS models. To achieve the purpose, we describe the scenario of advertiser perspective, processes, participants and their benefits of CPCD model. Especially, we suggest the new value in online coupon; "possibility of storage" and "complement for delivery to the target group". We also analyze the working condition for advertiser by a comparison of CPC and CPCD models through advertising expenditure simulation. The result of simulation implies that the CPCD model suits more properly to advertisers with medium-low price products rather than that of high priced goods. This denotes that since most of advertisers in CPC model are dealing with medium-low priced products, the result is very interesting. At last, we contemplate applicability of CPCD model in ubiquitous environment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.225-227
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• 2018

This research paper examines the history and present of 'temporary passengers' prescribed in Paragraph 9 of Article 5 of the Ships Safety Act Enforcement Regulations and suggests improvement plans referring to the examples of legislation of other countries. In 2015, Ministry of Ocean and Fisheries made authoritative interpretation that Paragraph 9 of Article 5 of the Ships Safety Act Enforcement Regulations, which prescribes special cargo drivers such as agricultural, marine or livestock vehicles as temporary passengers, is applied only to passenger ships and not to cargo ships such including Ro-Ro cargo ships. As the authoritative interpretation of the Ministry does not agree with not only the traditional interpretational methodology but also the interpretational methodology that are commonly used today, it lacks logical basis and looks unpersuasive. Paragraph 9 of Article 5 of the Ships Safety Act Enforcement Regulations can be applied not only on passenger ships but also on cargo ships. Also in case of Ro-Ro cargo ships, it is logically contradictory and against fairness not to acknowledge special cargo vehicle drivers as temporary passengers when there is no problem with safe navigation and safety of passengers on board even when the sailor, the sailor's family and the ship owner may be acknowledged as temporary passengers. To avoid unnecessary disputes and lawsuits, improvement plans using theory of legislation through statutory reform is more desirable. Therefore, the P aragraph should be amended to "Supercargo who deals with cargo that requires special care due to the characteristics of the cargo, such as transportation vehicles for agricultural products, marine products, livestock, explosives or flammable materials (drivers can serve as supercargos)" to reflect the distinct characteristics of cargo and ship navigation in Korea including the current distribution system, while setting an objective standard based on common sense of ordinary people and not on arbitrary interpretation.

• Archives of design research
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• v.18 no.3 s.61
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• pp.199-208
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• 2005

This study analysis if the concepts of kitsch and vernacular remains as cultural elements for product design. Because their function and aesthetic value have vivid possibilities for general product design fields. For the purpose, this study limits its range within post modernism, kitsch, and vernacular, and analyzes their relationships. Against functionalism, post modernism had cultural pluralisms to approach into popular styles, and some of them was amusing design. That post modernism designs stimulated human beings' emotion by decorations or some symbolic forms from specific objects is similar to the symbolism, regionalism, or pluralism of kitsch or vernacular. Kitsch is a free style that is not limited in any specific trends. It is a Meta culture that has influenced into various fields including design, so kitsch does not have a parallel position with a product or design. In product design, kitsch is the behavior and result of imitating existing objects' images. It could have amusement according to which objects are imitated. So if human beings feel amusement by kitsch, it could be same as the direction of post modernism. Kitsch is determined by design atmospheres. They cannot be specified abjectly, and can be different according to people. With symbolism and regionalism, kitsch and vernacular appeared according to people's needs. While kitsch is consumer's tastes-oriented, vernacular is cultural tradition-oriented. Kitsch has symbolism that specifies products' functions or design concepts, and it is a communication method between human being and products. Because vernacular is province-oriented, it has a lot of styles according to regional living environments and cultural differences. So vernacular design reflects continued traditional lifestyles. By restorative memory, regionalism, cultural pluralism, amusement, and symbolism, kitsch and vernacular could be understood the sub or parallel concepts of post modernism. They might be easily miss-understood mixed concepts that have western and national characters. But in kitsch and vernacular concept, modernizing pas by using the pluralism of post modernism should be considered positive. So, the range of the further study is also supposed to be focused on more widened fields to, to establish cultural identification in design.

• 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 Internet Computing and Services
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• v.22 no.1
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• pp.109-135
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• 2021

his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.

• Korean small business review
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• v.43 no.2
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• pp.59-86
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• 2021

The platform concept has been used for business operations in various forms: product platforms, transaction platforms and industry platforms. All these platforms have common characteristics of having 'core' that is reused frequently and 'peripherals' that are less reusable and changed often. Companies use platforms to enable efficient development and creation of product family, transactions and innovation. These platforms provide new opportunities for many small and medium-sized companies (SMEs) by bringing changes to traditional industrial structures focused on the products rather than platforms. The service robotics industry in South Korea is mainly composed of technology-intensive SMEs due to its small market size. Although these SMEs succeed in developing technologies, they have difficulties creating and expanding markets to sell products. Thus, this study addresses the characteristics and problems of the South Korean service robotics industry and analyses how ROBOPRINT, one of the SMEs in the service robotics industry, successfully creates and continuously expands the service robot market by adopting platform concept. The results indicate that ROBOPRINT has been applying two types of platforms: product and transaction platforms. First, ROBOPRINT created art robots that were apartment mural service robots. Rather than selling art robots, the company developed various robots such as painting robots, building exterior wall-cleaning robots by reusing the core technology of the robots. The company also developed various robots according to the buyers request. In addition, the company used the robots to directly provide apartment mural services for customers. This mural service has been extended into various areas, not only in apartments but also in soundproof walls, underground passages, and retaining walls. Besides, ROBOPRINT added new services continuously by developing technologies such as virtual reality. Second, ROBOPRINT mediated mural service buyers and mural designers. This platform reduced buyers' workload, which necessitates requesting mural services to ROBOPRINT and searching for mural designers. For designers, this opened up new opportunities to participate in the mural business. The platform attracted both mural buyers and designers who were scattered before. Finally, ROBOPRINT seeks to expand the platform's scope to outside company. To share internally reused ROBOPRINT's technology with other companies, the company participated in Daegu city's 'New Technology Platform Industry'. Furthermore, ROBOPRINT is trying to share the service platform by leasing robots to other companies. This allows external agents to develop technologies and provide services by reusing resources from ROBOPRINT. This study contributes to existing theories by showing that SMEs continuously create and expand markets by building various platforms. Moreover, it provides useful implications for practitioners by describing the firm's specific platform-building strategy.

• Journal of Life Science
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• v.34 no.4
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• pp.254-263
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• 2024

Korean jang is a food made using fermented soybeans, and the typical products include gochujang (GO), doenjang (DO), cheonggukjang (CH), and ganjang (GA). In this study, 16S rRNA metagenome analysis was performed on a total of 200 types of GO, DO, CH, and GA using next-generation sequencing to analyze the microbial community of fermented soybean foods and compare taxonomic (biomarker) differences. Alpha diversity analysis showed that in the CHAO index, the species richness index tended to be significantly higher compared to the DO and GA groups (p<0.001). The results of the microbial distribution analysis of the GO, DO, CH, and GA products showed that at the order level, Bacillales was the most abundant in the GO, DO, and CH groups, but Lactobacillales was most abundant in the GA group. Linear discriminant analysis effect (LEfSe) analysis was used to identify biomarkers at the family and species levels. Leuconostocaceae, Thermoactinomycetaceae, Bacillaceae, and Enterococcaceae appeared as biomarkers at the family level, and Bacillus subtilis, Kroppenstedtia sanguinis, Bacillus licheniformis, and Tetragenococcus halophilus appeared at the species level. Permutational multivariate analysis of variance (PERMANOVA) analysis showed that there was a significant difference in the microbial community structure of the GO, DO, CH, and GA groups (p=0.001), and the microbial community structure of the GA group showed the greatest difference. This study clarified the correlation between the characteristics of Korean fermented foods and microbial community distribution, enhancing knowledge of microorganisms participating in the fermentation process. These results could be leveraged to improve the quality of fermented soybean foods.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.