• The Journal of Society for e-Business Studies
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• v.17 no.4
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• pp.69-93
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• 2012

Various influence-related relationships in Social Network Services (SNS) among users, posts, and user-and-post, can be expressed using links. The current research evaluates the influence of specific users or posts by analyzing the link structure of relevant social network graphs to identify influential users. We applied the concept of mutual interactions proposed for ranking semantic web resources, rather than the voting notion of Page Rank or HITS, to blogosphere, one of the early SNS. Through many experiments with network models, where the performance and validity of each alternative approach can be analyzed, we showed the applicability and strengths of our approach. The weight tuning processes for the links of these network models enabled us to control the experiment errors form the link weight differences and compare the implementation easiness of alternatives. An additional example of how to enter the content scores of commercial or spam posts into the graph-based method is suggested on a small network model as well. This research, as a starting point of the study on identifying influential users in SNS, is distinctive from the previous researches in the following points. First, various influence-related properties that are deemed important but are disregarded, such as scraping, commenting, subscribing to RSS feeds, and trusting friends, can be considered simultaneously. Second, the framework reflects the general phenomenon where objects interacting with more influential objects increase their influence. Third, regarding the extent to which a bloggers causes other bloggers to act after him or her as the most important factor of influence, we treated sequential referencing relationships with a viewpoint from that of PageRank or HITS (Hypertext Induced Topic Selection).

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Korea Port Economic Association
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• v.32 no.1
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• pp.29-58
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• 2016

The purpose of this paper is to show the clustering trend and the comparison of empirical results and is to choose the clustering ports for 3 Korean ports(Busan, Incheon and Gwangyang Ports) by using the bootstrapped DEA(Data Envelopment Analysis) and game Cross-efficiency models for 38 Asian ports during the period 2003-2013 with 4 input variables(birth length, depth, total area, and number of cranes) and 1 output variable(container TEU). The main empirical results of this paper are as follows. First, bootstrapped DEA efficiency of SW and LT is 0.7660, 0.7341 respectively. Clustering results of the bootstrapped DEA analysis show that 3 Korean ports [ Busan (6.46%), Incheon (3.92%), and Gwangyang (2.78%)] can increase the efficiency in the SW model, but the LT model shows clustering values of -1.86%, -0.124%, and 2.11% for Busan, Gwangyang, and Incheon respectively. Second, the game cross-efficiency model suggests that Korean ports should be clustered with Hong Kong, Shanghi, Guangzhou, Ningbo, Port Klang, Singapore, Kaosiung, Keelong, and Bangkok ports. This clustering enhances the efficiency of Gwangyang by 0.131%, and decreases that of Busan by-1.08%, and that of Incheon by -0.009%. Third, the efficiency ranking comparison between the two models using the Wilcoxon Signed-rank Test was matched with the average level of SW (72.83 %) and LT (68.91%). The policy implication of this paper is that Korean port policy planners should introduce the bootstrapped DEA, and game cross-efficiency models when clustering is needed among Asian ports for enhancing the efficiency of inputs and outputs. Also, the results of SWOT(Strength, Weakness, Opportunity, and Threat) analysis among the clustering ports should be considered.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Journal of Korea Port Economic Association
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• v.33 no.2
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• pp.53-82
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• 2017

The purpose of this study is to show the clustering trend and compare empirical results, as well as to choose the clustering ports for 3 Korean ports (Busan, Incheon, and Gwangyang) by using meta-frontier DEA (Data Envelopment Analysis) and integer models on 38 Asian container ports over the period 2005-2014. The models consider 4 input variables (birth length, depth, total area, and number of cranes) and 1 output variable (container TEU). The main empirical results of the study are as follows. First, the meta-frontier DEA for Chinese seaports identifies as most efficient ports (in decreasing order) Shanghai, Hongkong, Ningbo, Qingdao, and Guangzhou, while efficient Korean seaports are Busan, Incheon, and Gwangyang. Second, the clustering results of the integer model show that the Busan port should cluster with Dubai, Hongkong, Shanghai, Guangzhou, Ningbo, Qingdao, Singapore, and Kaosiung, while Incheon and Gwangyang should cluster with Shahid Rajaee, Haifa, Khor Fakkan, Tanjung Perak, Osaka, Keelong, and Bangkok ports. Third, clustering through the integer model sharply increases the group efficiency of Incheon (401.84%) and Gwangyang (354.25%), but not that of the Busan port. Fourth, the efficiency ranking comparison between the two models before and after the clustering using the Wilcoxon signed-rank test is matched with the average level of group efficiency (57.88 %) and the technology gap ratio (80.93%). The policy implication of this study is that Korean port policy planners should employ meta-frontier DEA, as well as integer models when clustering is needed among Asian container ports for enhancing the efficiency. In addition Korean seaport managers and port authorities should introduce port development and management plans accounting for the reference and clustered seaports after careful analysis.

• Journal of Korea Port Economic Association
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• v.31 no.4
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• pp.39-52
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• 2015

The purpose of this paper is to empirically examine whether there are significant relations among rankings of cross-efficiency, web accessibility, and website evaluation. For this purpose, the study uses the KWAH-4 method developed by the Web Accessibility Laboratory in Korea, website evaluation method developed by the Business Development Bank of Canada (BDC), and the cross-efficiency model for 13 Asian container seaports including Korean, Chinese, and Japanese main ports in 3 years (2009, 2010, and 2013) using data for two cases: three inputs (depth, total area, and number of crane) and one output (TEU) in the first case and three inputs and two outputs (TEU and BDC overall score) in the second case. The main empirical results are as follows. First, the ranking orders of cross-efficiency, web accessibility, and website evaluation overall scores are not significantly correlated with each other. Second, if the BDC overall score is included in the output element, the correlation results are improved. However, the correlation coefficient is still low. The container port policy planners should introduce and consider the web accessibility and website evaluation scores when evaluating an efficiency-increasing plan for Korea's main container ports.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.5
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• pp.141-162
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• 2017

The International Monetary Fund (IMF) crisis introduced a system for easy layoffs. With recent economic downturn, employees have been asked to retire early and less new jobs have become available. More small businesses as a result have been started. The purpose of this research is to study weight and ranking on SWOT factors of korea food service franchise industry using the SWOT analysis. The Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used to analyze the SWOT found by the surveys. First, the SWOT analysis shows that the franchise owners and the expert group view the industry positively overall and there are more strengths, opportunities than weaknesses, threats. While there are negatives and threats to the industry overall, many people think that there are more opportunities and positive aspects. Second, the franchise owners rank proven business model and platform (S3) as the strongest strength of food service franchise businesses while the expert group ranks management supports (S2) from headquarters as the strongest strength. Third, the expert group and franchise owner group indicate that the weight on unfair franchise contracts with headquarters(W3) and high penalty from breaking a franchise agreement(W4) are 60% of weaknesses. Fourth, both the expert group and franchise owner group indicate that change in people's lifestyle, value system and consumption pattern(O3) as the most important opportunity. Fifth, both groups indicate that changes in consumption pattern(T1) due to ever changing food service industry as the biggest threat. It is ranked higher than the entry of korea food service franchises.

• Journal of Navigation and Port Research
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• v.32 no.8
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• pp.667-674
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• 2008

In this paper, operational problems of Busan Port and competitiveness deciding factors are analyzed by field data research and interview with employees in shipping companies and terminal operation companies. In the analysis, the problems Busan Port currently has are identified as follows: 1) low price competitiveness, 2) Lack of new port back facilities and connecting transportion system 3) Lack of operation ability of container terminal 4) Inefficient pour labor supply system. In order to strengthen the competitiveness and leap up to a hub port in North East Asia, Busan port is investigated to enhance below requirements. 1) Hiring more equipment and increase productivity in terminal 2) Integrating terminal operation companies to react maximization of vessel 3) Maintaining cost advantages 4)Proactively inviting global carriers to participate in the terminal operation 5)Making business environment for Global Terminal Operator to participate in the terminal operation in order to take advantage of their global marketing power.

• Journal of Navigation and Port Research
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• v.44 no.4
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• pp.319-325
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• 2020

The Korean government has been pursuing many supporting programs to enhance the competition of water transportation companies in recent years. To implement the policies effectively, which needs its monitering and evaluates about their business performance. The purpose of this study was to evaluate the business performance of water transportation company groups and determine the outranking between the groups using the Integrated Fuzzy AHP-PROMETHEE.. To achieve this purpose, first, the companies were classified into seven alternative company groups and the criteria for their evaluation was extracted Second, the weights of the criteria, by maritime and port expert survey, were calculated using the Fuzzy AHP. This paper, finally, determined the total priority orders of their company groups as the link Fuzzy PROMETHEE II with weights of the criteria and the local priority orders between them using the Fuzzy PROMETHEE I. In the proposal for this model, thus was collected four criteria such as growth ability, beneficial ability, technical ability, and productive ability. Through the result of this evaluation, the other marine transportation services group was determined as the highest outranking but the inland passenger & cargo transportation services group was lowest. Thus, the developing plan of the productive ability for the other marine transportation services group should be reviewed to continue its good performance, and all off the criteria for the inland passenger & cargo transportation services group to raise the performance should be reviewed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.93-99
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• 2017

The rate of traffic accidents that occurred in Korea last year is 10 out of every 100,000 people, ranking it 6th among the 35 OECD member countries. The accident rate of children with disabilities and elderly people is also high. The purpose of this study is to introduce traffic safety facilities which have been developed for the reduction of traffic accidents in non-urban areas in Korea through an analysis of the related literature, the accident factors using traffic accident analysis system data and traffic accident characteristics. Traffic safety integrated facilities for ICT-based pedestrian crossings are subject to cross-sectional coverage of child protection zones. The smart safety fence prevents vehicles from parking illegally and informs pedestrians that there is an access vehicle on the pedestrian crossing. The smart bump is designed to warn drivers who are not aware of the pedestrians. In order to standardize the appropriate form and size of the traffic safety facilities for pedestrian crossings, we constructed a standard model for each type, considering the road function, press classification, power, lane number, geometric form, etc. As a result, the rate of traffic accidents involving vulnerable people was reduced. In addition, it is anticipated that the maintenance costs will be reduced by the use of a solar power supply and their compatibility with the existing installed safety fences.