• Journal of Information Technology Applications and Management
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• v.26 no.5
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• pp.57-65
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• 2019

Recommendation Systems are information technologies that E-commerce merchants have adopted so that online shoppers can receive suggestions on items that might be interesting or complementing to their purchased items. These systems stipulate valuable assistance to the user's purchasing decisions, and provide quality of push service. Traditionally, Recommendation Systems have been designed using a centralized system, but information service is growing vast with a rapid and strong scalability. The next generation of information technology such as Cloud Computing and Big Data Environment has handled massive data and is able to support enormous processing power. Nevertheless, analytic technologies are lacking the different capabilities when processing big data. Accordingly, we are trying to design a conceptual service model with a proposed new algorithm and user adaptation on dynamic recommendation service for big data environment.

• Journal of the Korean Society of Food Culture
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• v.29 no.6
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• pp.549-556
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• 2014

This study aimed to determine consumer satisfaction according to selection attributes of specialized coffee shops and also understand the effects of consumer satisfaction on recommendation intention and revisit intention. Through positive analysis, the study produced the following results. In the factor analysis of selection attributes of specialized coffee shops, there were six factors: 'quality', 'brand image', 'economic feasibility', 'menu diversity', 'the atmosphere and convenience of the shop', and 'service'. Among these factors, 'brand image', 'economic feasibility', and 'menu diversity' were found to exert a significant influence on consumer satisfaction. Second, consumer satisfaction had a significant influence on recommendation intention and revisit intention. Third, consumer intention to revisit specialized coffee shops showed a significant influence on recommendation intention.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.320-325
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• 2002

Today many companies offer millions of products to customers. They are faced with a problem to choose particular products . In response to this problem a new marking strategy, recommendation has emerged. Among recommendation technologies collaborative filtering is most preferred. But the performance degrades with the number of customers and products. Namely, collaborative filtering has two major limitations, sparsity and scalability. To overcome these problems we introduced a new recommendation methodology using adjusted product hierarchy, grain. This methodology focuses on dimensionality reduction to improve recommendation quality and uses a marketer's specific knowledge or experience. In addition, it uses a new measure in the neighborhood formation step which is the most important one in recommendation process.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.342-351
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• 2002

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.187-202
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• 2022

The purpose of this study is to, focusing on public rental housing, examine the influence of the management and living environment factors of multi-unit residential buildings upon their satisfaction with living, brand loyalty, and intent of recommendation, and investigate the mediating effect of the satisfaction with living. For this study, the researcher surveyed the residents of public rental housing facilities in Seoul and Gyeonggi Province, which resulted in the analysis of 331 responses. The data was analyzed using SPSS 23.0 and AMOS 23.0 programs. The findings of this study are as follows; The management and living environment quality factors of public rental housing facilities had a direct impact on the satisfaction with living, brand loyalty, and intent of recommendation. And, the sub-factors of the management and living environment quality that influenced each variable were different among themselves. Also, the satisfaction level with living in public rental housing facilities had a significant impact on the intent of recommendation, while it had a mediating effect in the relationship between the management and living environment quality factors of public rental housing facilities and the intent of recommendation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3419-3437
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• 2022

Anonymization technology is an important technology for privacy protection in the process of data release. Usually, before publishing data, the data publisher needs to use anonymization technology to anonymize the original data, and then publish the anonymized data. However, for data publishers who do not have or have less anonymized technical knowledge background, how to configure appropriate parameters for data with different characteristics has become a more difficult problem. In response to this problem, this paper adds a historical configuration scheme resource pool on the basis of the traditional anonymization process, and configuration parameters can be automatically recommended through the historical configuration scheme resource pool. On this basis, a privacy model hybrid recommendation algorithm for user satisfaction is formed. The algorithm includes a forward recommendation process and a reverse recommendation process, which can respectively perform data anonymization processing for users with different anonymization technical knowledge backgrounds. The privacy model hybrid recommendation algorithm for user satisfaction described in this paper is suitable for a wider population, providing a simpler, more efficient and automated solution for data anonymization, reducing data processing time and improving the quality of anonymized data, which enhances data protection capabilities.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.1-22
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• 2016

News contents has been collected, selected, edited and sometimes distorted by the news recommendation mechanisms of online portals in nowadays. Prior studies had not confirmed the consensus of newsworthiness, and they had not tried to empirically validate the impacts of newsworthiness on audience reactions. This study challenged to summarize the concepts of newsworthiness and validate the impact of representativeness of both editor's and audience's perspective on audience reactions as perceived news quality, trust on news portal, perceived usefulness, service satisfaction, loyalty, continuous usage intention, and word-of-mouth intention by adopting the representativeness heuristics method and information adoption model. 357 valid data had been collected using a scenario survey method. Subjects in each groups are exposed by 3 news recommendation mechanisms: 1) the time-priority news exposure mechanism (control group), 2) the reference-score-based news recommendation mechanism (a single treatment group), and 3) the major-news-priority exposure mechanism sorting by the reference scores made by peer audiences (the mixed treatment group). Data had been analyzed by the MANOVA and PLS method. MANOVA results indicate that only mixed method of both editor and audience recommendation mechanisms impacts on perceived news quality and trust. PLS results indicate that perceived news quality and trust could significantly affect on the perceived usefulness, service satisfaction, loyalty, continuance usage, and word-of-mouth intention. This study would contributions to empathize the role of information technology in media industry, to conceptualize the news value in the balanced views of both editors and audiences, and to empirically validate the benefits of news recommendation mechanisms in academy. For practice, the results of this study suggest that online news portals would be better to make mixed news recommendation mechanisms to attract audiences.

• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.239-250
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• 2004

The preference of customers change as time goes by. The existing Collaborative Filtering (CF) techniques has no room for including this change yet, although these techniques have been known to be the most successful recommendation technique that has been used in a number of different applications. In this study, we proposed a new methodology for enhancing the quality of recommendation using the customers' dynamic behaviors over time. The proposed methodology is applied to a large department store in Korea, compared to existing CF techniques. Some experiments on the real world data show that the proposed methodology provides higher quality recommendations than other CF techniques, especially better performance on heavy users.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.297-306
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• 2022

This study aims to verify the influence of coffee education service quality recognized by trainees wishing to get a job at coffee-related companies on job preparation behavior through education satisfaction and recommendation intention. In order to achieve the research purpose, this study posited five research hypotheses based on relative literature and also established a research model with the five hypotheses. This study shows the following research results. First, the study found that coffee education service quality had a positive and significant impact on education satisfaction. Second, the study found that educational satisfaction had a positive and significant impact on recommendation intention. Third, the study found that educational satisfaction had a positive and significant impact on job preparation behavior. Fourth, the study found that education satisfaction had a positive and significant impact on the effect of coffee education service quality on recommendation intention. Fifth, the study found that education satisfaction had a positive and significant impact on the effect of coffee education service quality on employment recommendation intention. Such findings of this study imply practical suggestions that the characteristics of a wide range of trainees in the study of coffee education service quality and satisfaction, and provide practical suggestions to help improve the future direction of education services and competitiveness of coffee education institutions.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.164-175
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• 2022

The OTT (Over The Top) platform promotes itself as a distinctive competitive advantage in that it allows users to stay on the platform longer and visit more often through a Personalized Recommendation Service. In this study, the characteristics of the Personalized Recommendation Service are divided into three categories: recommendation accuracy, recommendation diversity, and recommendation novelty. Then proposed a research model which affects the usefulness of users to recognize recommendation services by each characteristics and leads to satisfaction of expectations. The result of conducting an online survey of 300 people in their 20s and 30s who subscribe Netflix shows that the perceived usefulness increased when the accuracy, variety, and novelty of Netflix's Recommendation Service were high. It was also confirmed that high perceived usefulness leads to satisfaction of expectations before and after Netflix use. The derived research results can confirm the importance of evaluating the personalized recommendation service in terms of user experience and provide implications for ways to improve the quality of recommendation services.