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Jae Kyeong Kim
School of Management & Department of Big Data Analytics, KyungHee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea

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Journal article
Published: 19 July 2021 in Sustainability
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Numerous reviews are posted every day on travel information sharing platforms and sites. Hotels want to develop a customer recommender system to quickly and effectively identify potential target customers. TripAdvisor, the travel website that provided the data used in this study, allows customers to rate the hotel based on six criteria: Value, Service, Location, Room, Cleanliness, and Sleep Quality. Existing studies classify reviews into positive, negative, and neutral by extracting sentiment terms through simple sentimental analysis. However, this method has limitations in that it does not consider various aspects of hotels well. Therefore, this study performs fine-tuning the BERT (Bidirectional Encoder Representations from Transformers) model using review data with rating labels on the TripAdvisor site. This study suggests a multi-criteria recommender system to recommend a suitable target customers for the hotel. As the rating values of six criteria of TripAdvisor are insufficient, the proposed recommender system uses fine-tuned BERT to predict six criteria ratings. Based on this predicted ratings, a multi-criteria recommender system recommends personalized Top-N customers for each hotel. The performance of the multi-criteria recommender system suggested in this study is better than that of the benchmark system, a single-criteria recommender system using overall ratings.

ACS Style

Yuanyuan Zhuang; Jaekyeong Kim. A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management. Sustainability 2021, 13, 8039 .

AMA Style

Yuanyuan Zhuang, Jaekyeong Kim. A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management. Sustainability. 2021; 13 (14):8039.

Chicago/Turabian Style

Yuanyuan Zhuang; Jaekyeong Kim. 2021. "A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management." Sustainability 13, no. 14: 8039.

Journal article
Published: 30 May 2021 in Sustainability
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Information technology and the popularity of mobile devices allow for various types of customer data, such as purchase history and behavior patterns, to be collected. As customer data accumulate, the demand for recommender systems that provide customized services to customers is growing. Global e-commerce companies offer recommender systems to gain a sustainable competitive advantage. Research on recommender systems has consistently suggested that customer satisfaction will be highest when the recommendation algorithm is accurate and recommends a diversity of items. However, few studies have investigated the impact of accuracy and diversity on customer satisfaction. In this research, we seek to identify the factors determining customer satisfaction when using the recommender system. To this end, we develop several recommender systems and measure their ability to deliver accurate and diverse recommendations and their ability to generate customer satisfaction with diverse data sets. The results show that accuracy and diversity positively affect customer satisfaction when applying a deep learning-based recommender system. By contrast, only accuracy positively affects customer satisfaction when applying traditional recommender systems. These results imply that developers or managers of recommender systems need to identify factors that further improve customer satisfaction with the recommender system and promote the sustainable development of e-commerce.

ACS Style

Jaekyeong Kim; Ilyoung Choi; Qinglong Li. Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches. Sustainability 2021, 13, 6165 .

AMA Style

Jaekyeong Kim, Ilyoung Choi, Qinglong Li. Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches. Sustainability. 2021; 13 (11):6165.

Chicago/Turabian Style

Jaekyeong Kim; Ilyoung Choi; Qinglong Li. 2021. "Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches." Sustainability 13, no. 11: 6165.

Journal article
Published: 29 January 2020 in Sustainability
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A recommender system supports customers to find information, products, or services (such as music, books, movies, web sites, and digital contents), so it could help customers to make rapid routine decisions and save their time and money. However, most existing recommender systems do not recommend items that are already purchased by the target customer, so are not suitable for considering customers’ repetitive purchase behavior or purchasing order. In this research, we suggest a multi-period product recommender system, which can learn customers’ purchasing order and customers’ repetitive purchase pattern. For such a purpose we applied the Recurrent Neural Network (RNN), which is one of the artificial neural network structures specialized in time series data analysis, instead of collaborative filtering techniques. Recommendation periods are segmented as various time-steps, and the proposed RNN-based recommender system can recommend items by multiple periods in a time sequence. Several experiments with real online food market data show that the proposed system shows higher performance in accuracy and diversity in a multi-period perspective than the collaborative filtering-based system. From the experimental results, we conclude that the proposed system is suitable for multi-period product recommendation, which results in robust performance considering well customers’ purchasing orders and customers’ repetitive purchase patterns. Moreover, in terms of sustainability, we expect that our study contributes to the reduction of food wastes by inducing planned consumption, and the reduction of shopping time and effort.

ACS Style

Hea In Lee; Il Young Choi; Hyun Sil Moon; Jae Kyeong Kim. A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks. Sustainability 2020, 12, 969 .

AMA Style

Hea In Lee, Il Young Choi, Hyun Sil Moon, Jae Kyeong Kim. A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks. Sustainability. 2020; 12 (3):969.

Chicago/Turabian Style

Hea In Lee; Il Young Choi; Hyun Sil Moon; Jae Kyeong Kim. 2020. "A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks." Sustainability 12, no. 3: 969.

Conference paper
Published: 22 January 2020 in Advances in Intelligent Systems and Computing
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In this research, a modified CF (Collaborative filtering) procedure is suggested to decide a destination for group travel planning service. Furthermore, a composite travel product includes hotel, and attraction in addition to the selection of a destination. For such a purpose, individual user’s personal requirements are represented by constraints, and a group constraints are constructed based on group member’s constraints. A two-phase group approximate satisfying constraint procedure is suggested to automate group decision making procedure for selecting their trip packages to improve group tourists’ convenience and their satisfaction. A group travel recommendation is suggested with an illustrative example and an experimental design is suggested to validate the suggested procedure.

ACS Style

Jinlu He; Ilyoung Choi; Jaekyeong Kim. A Group Travel Recommender System Based on Collaborative Filtering and Group Approximate Constraint Satisfaction. Advances in Intelligent Systems and Computing 2020, 1178 -1183.

AMA Style

Jinlu He, Ilyoung Choi, Jaekyeong Kim. A Group Travel Recommender System Based on Collaborative Filtering and Group Approximate Constraint Satisfaction. Advances in Intelligent Systems and Computing. 2020; ():1178-1183.

Chicago/Turabian Style

Jinlu He; Ilyoung Choi; Jaekyeong Kim. 2020. "A Group Travel Recommender System Based on Collaborative Filtering and Group Approximate Constraint Satisfaction." Advances in Intelligent Systems and Computing , no. : 1178-1183.

Articles
Published: 16 March 2019 in Asia Pacific Journal of Tourism Research
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In a smart tourism ecosystem, travel communication websites play a critical role in choosing destinations and hotels. This research suggests a travel recommender system for automating word-of-mouth (WOM) effects and providing personalized travel-planning services to tourists. Collaborative filtering (CF)-based recommender systems have been extensively employed for personalization services in diverse areas; the basic principle of CF is WOM communication. This research proposes a travel recommender system that helps a tourist build his/her personalized travel plan based on CF and constraint satisfaction filtering. Constraint satisfaction filtering is adopted to profile a tourist’s needs and circumstances. For this purpose, this research modifies the existing constraint satisfaction method to an approximate constraint satisfaction filtering method that incorporates indifference intervals into constraints. We build a prototype system and a benchmark system to evaluate the effectiveness, usability, and novelty of the proposed travel recommender system. The experimental results demonstrate a methodology for performing personalized tourist’s travel planning and automating WOM communication outperforms the benchmark system.

ACS Style

Il Young Choi; Young U. Ryu; Jae Kyeong Kim. A recommender system based on personal constraints for smart tourism city. Asia Pacific Journal of Tourism Research 2019, 26, 440 -453.

AMA Style

Il Young Choi, Young U. Ryu, Jae Kyeong Kim. A recommender system based on personal constraints for smart tourism city. Asia Pacific Journal of Tourism Research. 2019; 26 (4):440-453.

Chicago/Turabian Style

Il Young Choi; Young U. Ryu; Jae Kyeong Kim. 2019. "A recommender system based on personal constraints for smart tourism city." Asia Pacific Journal of Tourism Research 26, no. 4: 440-453.

Journal article
Published: 07 August 2018 in Online Information Review
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Purpose Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers. Design/methodology/approach This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems. Findings The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations. Originality/value Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations.

ACS Style

Jae Kyeong Kim; Hyun Sil Moon; Byong Ju An; Il Young Choi. A grocery recommendation for off-line shoppers. Online Information Review 2018, 42, 468 -481.

AMA Style

Jae Kyeong Kim, Hyun Sil Moon, Byong Ju An, Il Young Choi. A grocery recommendation for off-line shoppers. Online Information Review. 2018; 42 (4):468-481.

Chicago/Turabian Style

Jae Kyeong Kim; Hyun Sil Moon; Byong Ju An; Il Young Choi. 2018. "A grocery recommendation for off-line shoppers." Online Information Review 42, no. 4: 468-481.

Journal article
Published: 30 September 2017 in Asia Pacific Journal of Information Systems
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ACS Style

Youngeui Kim; Hyun Sil Moon; Jae Kyeong Kim. Analyzing the Effect of Electronic Word of Mouth on Low Involvement Products. Asia Pacific Journal of Information Systems 2017, 27, 139 -155.

AMA Style

Youngeui Kim, Hyun Sil Moon, Jae Kyeong Kim. Analyzing the Effect of Electronic Word of Mouth on Low Involvement Products. Asia Pacific Journal of Information Systems. 2017; 27 (3):139-155.

Chicago/Turabian Style

Youngeui Kim; Hyun Sil Moon; Jae Kyeong Kim. 2017. "Analyzing the Effect of Electronic Word of Mouth on Low Involvement Products." Asia Pacific Journal of Information Systems 27, no. 3: 139-155.

Journal article
Published: 31 March 2017 in Journal of Intelligence and Information Systems
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ACS Style

Min Kyu Jung; Il Young Choi; Jae Kyeong Kim. Emotion Detection Model based on Sequential Neural Networks in Smart Exhibition Environment. Journal of Intelligence and Information Systems 2017, 23, 109 -126.

AMA Style

Min Kyu Jung, Il Young Choi, Jae Kyeong Kim. Emotion Detection Model based on Sequential Neural Networks in Smart Exhibition Environment. Journal of Intelligence and Information Systems. 2017; 23 (1):109-126.

Chicago/Turabian Style

Min Kyu Jung; Il Young Choi; Jae Kyeong Kim. 2017. "Emotion Detection Model based on Sequential Neural Networks in Smart Exhibition Environment." Journal of Intelligence and Information Systems 23, no. 1: 109-126.

Journal article
Published: 01 June 2016 in International Journal of Information Management
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Online video recommender systems help users find videos suitable for their preferences. However, they have difficulty in identifying dynamic user preferences. In this study, we propose a new recommendation procedure using changes of users’ facial expressions captured every moment. Facial expressions portray the users’ actual emotions about videos. We can utilize them to discover dynamic user preferences. Further, because the proposed procedure does not rely on historical rating or purchase records, it properly addresses the new user problem, that is, the difficulty in recommending products to users whose past rating or purchase records are not available. To validate the recommendation procedure, we conducted experiments with footwear commercial videos. Experiment results show that the proposed procedure outperforms benchmark systems including a random recommendation, an average rating approach, and a typical collaborative filtering approach for recommendation to both new and existing users. From the results, we conclude that facial expressions are a viable element in recommendation.

ACS Style

Il Young Choi; Myung Geun Oh; Jae Kyeong Kim; Young U. Ryu. Collaborative filtering with facial expressions for online video recommendation. International Journal of Information Management 2016, 36, 397 -402.

AMA Style

Il Young Choi, Myung Geun Oh, Jae Kyeong Kim, Young U. Ryu. Collaborative filtering with facial expressions for online video recommendation. International Journal of Information Management. 2016; 36 (3):397-402.

Chicago/Turabian Style

Il Young Choi; Myung Geun Oh; Jae Kyeong Kim; Young U. Ryu. 2016. "Collaborative filtering with facial expressions for online video recommendation." International Journal of Information Management 36, no. 3: 397-402.

Journal article
Published: 30 June 2014 in Journal of the Korea society of IT services
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ACS Style

Jae-Ho Choe; Hyun-Sil Moon; Jae-Kyeong Kim. Developing Performance Indicator for Smart-Exhibition. Journal of the Korea society of IT services 2014, 13, 71 -82.

AMA Style

Jae-Ho Choe, Hyun-Sil Moon, Jae-Kyeong Kim. Developing Performance Indicator for Smart-Exhibition. Journal of the Korea society of IT services. 2014; 13 (2):71-82.

Chicago/Turabian Style

Jae-Ho Choe; Hyun-Sil Moon; Jae-Kyeong Kim. 2014. "Developing Performance Indicator for Smart-Exhibition." Journal of the Korea society of IT services 13, no. 2: 71-82.

Journal article
Published: 01 June 2014 in Multimedia Tools and Applications
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Recent years have witnessed the rapid growth of performing arts in Korea as well as worldwide. Advances in performing arts technologies have allowed for a shift from one-way performances to interactive ones. The audience’s arousal is one of the most important features in interactive performances. An audience consists of a group of people responding collectively to a stimulus. Here arousal is a critical factor influencing audience satisfaction and can be measured based on the audience’s behavior. The total group arousal of an audience is formed by exchanging emotional effects with surroundings. In this regard, empirical approaches are not sufficient in comparison to various theoretical approaches to group arousal. Previous studies have generally evaluated group arousal by the sum of group members’ emotions recognized from their faces, gestures, or voice. However, it is not easy to apply real-time data from individuals to performing arts. In addition, it is difficult to set sensors for audiences to retrieve human data. In this regard, this paper proposes a method for empirically measuring group arousal based on the rapid movement synchronization of a given group. In the proposed method, the extent to which each member’s movement response is synchronized with differential images and histograms is measured first, and then group arousal is calculated by the degree of this synchronization. The performance of the proposed method is evaluated through an experiment by setting a threshold for deciding whether there is a response to a stimulus. The experimental results for 15 groups indicate the accuracy of the proposed method to be 82 %.

ACS Style

Seung-Bo Park; Joon Mo Ryu; Jae Kyeong Kim. A group arousal analysis based on the movement synchronization of audiences. Multimedia Tools and Applications 2014, 74, 6431 -6442.

AMA Style

Seung-Bo Park, Joon Mo Ryu, Jae Kyeong Kim. A group arousal analysis based on the movement synchronization of audiences. Multimedia Tools and Applications. 2014; 74 (16):6431-6442.

Chicago/Turabian Style

Seung-Bo Park; Joon Mo Ryu; Jae Kyeong Kim. 2014. "A group arousal analysis based on the movement synchronization of audiences." Multimedia Tools and Applications 74, no. 16: 6431-6442.

Journal article
Published: 31 August 2013 in International Journal of Information Management
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As exhibitions are known to play important roles in marketing and sales promotion, the exhibition industry has grown significantly not only in the exhibition event size and frequency but also in the number of participating firms and visitors. While the challenge in assessing economic returns from exhibitions is being studied, it is agreed that the eventual success of an exhibition resides largely in its ability to meet the visitors’ needs. Visitors use an exhibition as a source of information when searching for products or services. Though an exhibition provides an information-rich environment, however, visitors often get lost in the abundance of information. A specialized recommender system can be a good solution to information overload as it can guide visitors to right exhibition booths and help them collect necessary information. Traditional collaborative-filtering recommender systems, however, use only customers’ rating or purchase records so that they do not capture exhibition visitors’ temporal visit sequences and dynamic preferences. Moreover, due to the computation overhead, they cannot generate real-time recommendation in ubiquitous environments for exhibitions. In order to overcome these drawbacks, this study proposes a booth recommendation procedure that takes into consideration not only booth visit records but also visit sequences. Experiment results show that the proposed procedure achieves higher recommendation accuracy, faster computation, and more diversity than a typical collaborative-filtering recommender system. From the results, we conclude that the proposed booth recommendation procedure is suitable for real-time recommendation in ubiquitous exhibition environments.

ACS Style

Hyun Sil Moon; Jae Kyeong Kim; Young U. Ryu. A sequence-based filtering method for exhibition booth visit recommendations. International Journal of Information Management 2013, 33, 620 -626.

AMA Style

Hyun Sil Moon, Jae Kyeong Kim, Young U. Ryu. A sequence-based filtering method for exhibition booth visit recommendations. International Journal of Information Management. 2013; 33 (4):620-626.

Chicago/Turabian Style

Hyun Sil Moon; Jae Kyeong Kim; Young U. Ryu. 2013. "A sequence-based filtering method for exhibition booth visit recommendations." International Journal of Information Management 33, no. 4: 620-626.

Book chapter
Published: 27 April 2013 in Information and Communication Technologies in Tourism 2013
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Our study of unplanned behaviour theory examines the effect of the booth recommender system (BRS) on the goals of exhibition attendees. Previous studies have overlooked the importance of the unplanned behavioural effectiveness of information technology (IT) devices for understanding motivation and delivering unexpected outcomes at exhibitions. In this paper, we distinguish several goal frames, including hedonic, gain, and normative goals, which contribute to the relationship between BRS use and unplanned boot visits. BRS use directly influences revisit intentions to an exhibition and contributes to unplanned booth visits. BRS use in an exhibition can contribute to attendees’ impulsive behaviour and can induce them to return to an exhibition. The results and implications are discussed.

ACS Style

Namho Chung; Chulmo Koo; Jae Kyeong Kim. Unplanned Behaviour of Exhibition Attendees and the Booth Recommender System: The Goal Framing Theory Perspective. Information and Communication Technologies in Tourism 2013 2013, 460 -471.

AMA Style

Namho Chung, Chulmo Koo, Jae Kyeong Kim. Unplanned Behaviour of Exhibition Attendees and the Booth Recommender System: The Goal Framing Theory Perspective. Information and Communication Technologies in Tourism 2013. 2013; ():460-471.

Chicago/Turabian Style

Namho Chung; Chulmo Koo; Jae Kyeong Kim. 2013. "Unplanned Behaviour of Exhibition Attendees and the Booth Recommender System: The Goal Framing Theory Perspective." Information and Communication Technologies in Tourism 2013 , no. : 460-471.

Journal article
Published: 31 March 2013 in Journal of Intelligence and Information Systems
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ACS Style

Joon Mo Ryu; Seung-Bo Park; Jae Kyeong Kim. A Study of the Reactive Movement Synchronization for Analysis of Group Flow. Journal of Intelligence and Information Systems 2013, 19, 79 -94.

AMA Style

Joon Mo Ryu, Seung-Bo Park, Jae Kyeong Kim. A Study of the Reactive Movement Synchronization for Analysis of Group Flow. Journal of Intelligence and Information Systems. 2013; 19 (1):79-94.

Chicago/Turabian Style

Joon Mo Ryu; Seung-Bo Park; Jae Kyeong Kim. 2013. "A Study of the Reactive Movement Synchronization for Analysis of Group Flow." Journal of Intelligence and Information Systems 19, no. 1: 79-94.

Journal article
Published: 22 February 2013 in Information Technology and Management
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Korea is one of a few countries that have achieved significant economic growth in the recent two decades. The early agricultural economy in 1940s and 1950s had gradually changed due to the government-driven economic development plans started in 1960s. The early emphasis was made on both modernization of the agricultural industry and the promotion of the heavy and chemical industry. After seeing the success in the heavy and chemical industry, the government and the private sector worked together toward more advancement in the heavy and chemical industry in 1970s. However, the government and some private firms realized the long-term potential in the electronics and telecommunications industry and started establishing infrastructure for electronics and telecommunications industry in 1980s. The blossoming of the information technology (IT) sector including the electronics and telecommunications industry and other related areas was observed in the late 1990s and beyond.

ACS Style

Young U. Ryu; Jae Kyeong Kim. Introduction to the special issue information technology in Korea: its role in firm success and economic development. Information Technology and Management 2013, 14, 1 -1.

AMA Style

Young U. Ryu, Jae Kyeong Kim. Introduction to the special issue information technology in Korea: its role in firm success and economic development. Information Technology and Management. 2013; 14 (1):1-1.

Chicago/Turabian Style

Young U. Ryu; Jae Kyeong Kim. 2013. "Introduction to the special issue information technology in Korea: its role in firm success and economic development." Information Technology and Management 14, no. 1: 1-1.

Journal article
Published: 30 September 2012 in Journal of the Korea society of IT services
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ACS Style

Pil Sik Chang; Il Young Choi; Ju Cheol Choi; Jae Kyeong Kim. Analysis of Billing System using AHP for Cloud Computing Services. Journal of the Korea society of IT services 2012, 11, 129 -159.

AMA Style

Pil Sik Chang, Il Young Choi, Ju Cheol Choi, Jae Kyeong Kim. Analysis of Billing System using AHP for Cloud Computing Services. Journal of the Korea society of IT services. 2012; 11 (3):129-159.

Chicago/Turabian Style

Pil Sik Chang; Il Young Choi; Ju Cheol Choi; Jae Kyeong Kim. 2012. "Analysis of Billing System using AHP for Cloud Computing Services." Journal of the Korea society of IT services 11, no. 3: 129-159.

Journal article
Published: 26 September 2012 in Information Technology and Management
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Korea, once a typical post-war underdeveloped country, has achieved significant economic growth, becoming one of the G20 economies. In particular, during the past decades, the information technology (IT) sector (including telecommunications, consumer electronics, and computer games) has occupied a great portion of Korea’s economy and the use of IT in non-IT sectors (such as manufacturing, logistics, banking, non-profit sectors, and retail services including e-commerce and m-commerce) has grown substantially [3]. Korea’s early economic success in 1970s and 1980s were mainly due to the development in heavy industries. In early 1990s, Korea experienced 80–10 % GDP growth rates. Then, the Asian financial crisis initiated in Thailand in the mid-1990s affected the Korean economy significantly. The unemployment rate reached up to 6.8 % in 1998 as several large Korean conglomerates including Hanbo, Sammi, Jinro, and Halla went bankrupt and financial sectors reported large loses. As a result of

ACS Style

Young U. Ryu; Jae Kyeong Kim; Il Young Choi. The role of IT in Korea’s economic development. Information Technology and Management 2012, 14, 3 -6.

AMA Style

Young U. Ryu, Jae Kyeong Kim, Il Young Choi. The role of IT in Korea’s economic development. Information Technology and Management. 2012; 14 (1):3-6.

Chicago/Turabian Style

Young U. Ryu; Jae Kyeong Kim; Il Young Choi. 2012. "The role of IT in Korea’s economic development." Information Technology and Management 14, no. 1: 3-6.

Journal article
Published: 05 September 2012 in Information Technology and Management
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Despite popular belief that timely and precise data are important and indispensable to good decisions and that good decisions are related to better firm performance, empirical research that examines the effect of data quality on firm performance is still scarce. How great an impact does data quality have on firm performance? This study empirically investigates the effect of firm-level data quality on firm performance in the Korean financial industry during 2008–2010. The results show that commercial banks have high-quality data, while credit unions have comparatively low-quality data. They also show that better data quality has a positive influence on sales, operating profit, and value added. Improving the level of data quality management maturity by one can increase firm performance by 33.7 % in sales, 64.4 % in operating profit, and 26.2 % in value added.

ACS Style

Jun Yong Xiang; Sangho Lee; Jae Kyeong Kim. Data quality and firm performance: empirical evidence from the Korean financial industry. Information Technology and Management 2012, 14, 59 -65.

AMA Style

Jun Yong Xiang, Sangho Lee, Jae Kyeong Kim. Data quality and firm performance: empirical evidence from the Korean financial industry. Information Technology and Management. 2012; 14 (1):59-65.

Chicago/Turabian Style

Jun Yong Xiang; Sangho Lee; Jae Kyeong Kim. 2012. "Data quality and firm performance: empirical evidence from the Korean financial industry." Information Technology and Management 14, no. 1: 59-65.

Journal article
Published: 01 September 2012 in Expert Systems with Applications
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Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10 years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.

ACS Style

Deuk Hee Park; Hyea Kyeong Kim; Il Young Choi; Jae Kyeong Kim. A literature review and classification of recommender systems research. Expert Systems with Applications 2012, 39, 10059 -10072.

AMA Style

Deuk Hee Park, Hyea Kyeong Kim, Il Young Choi, Jae Kyeong Kim. A literature review and classification of recommender systems research. Expert Systems with Applications. 2012; 39 (11):10059-10072.

Chicago/Turabian Style

Deuk Hee Park; Hyea Kyeong Kim; Il Young Choi; Jae Kyeong Kim. 2012. "A literature review and classification of recommender systems research." Expert Systems with Applications 39, no. 11: 10059-10072.

Conference paper
Published: 01 January 2012 in Business Information Systems
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Introduced is a neural network method to build survival time prediction models with censored and completed observations. The proposed method modifies the standard back-propagation neural network process so that the censored data can be used without alteration. On the other hand, existing neural network methods require alteration of censored data and suffer from the problem of scalability on the prediction output domain. Further, the modification of the censored observations distorts the data so that the final prediction outcomes may not be accurate. Preliminary validations show that the proposed neural network method is a viable method.

ACS Style

Young U. Ryu; Jae Kyeong Kim; Kwang Hyuk Im; Hankuk Hong. Neural Network Analysis of Right-Censored Observations for Occurrence Time Prediction. Business Information Systems 2012, 108, 100 -109.

AMA Style

Young U. Ryu, Jae Kyeong Kim, Kwang Hyuk Im, Hankuk Hong. Neural Network Analysis of Right-Censored Observations for Occurrence Time Prediction. Business Information Systems. 2012; 108 ():100-109.

Chicago/Turabian Style

Young U. Ryu; Jae Kyeong Kim; Kwang Hyuk Im; Hankuk Hong. 2012. "Neural Network Analysis of Right-Censored Observations for Occurrence Time Prediction." Business Information Systems 108, no. : 100-109.