• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.279-284
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• 2018

With the availability of real-time student behavioral data, personalization on education is gaining a huge traction. Data collected from massively open online courses (MOOC) has shifted the content delivery method from fixed, static to user-adopted form. Such educational content can be personalized by student's level of achivement. In this paper, we propose a service that automates the content restructuring, based on dynamic profile. With the student behavioral data, the proposed service restructures educational content by changing the order, extending and shrinking the published material. To do this, we record students' behavioral data and content information as a metadata, which will be used to generate dynamic profile.

• Journal of Korea Trade
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• v.23 no.7
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• pp.45-63
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• 2019

Purpose - This study aims to examine the factors that affect consumers' intention to use a global business-to-consumer sharing platform. Design/methodology - The questionnaire collected 300 copies from June 25 to July 11, 2019, of which 281 were used for statistical processing. The structural equation model (SEM) was used to test hypothesis in this research. Findings - The results showed that information innovation, personalization, and personal innovation influenced perceived usefulness, and social connectivity did not affect perceived usefulness. And perceived usefulness greatly influenced the intention to use. Research limitations/implications - The limitations of the study are that most of the survey respondents were in their twenties and could not grasp the perception of sharing economy services for various age groups. This paper derived implications that sharing platform promotes sharing and cooperation, which are the basic principles of international trade, to increase the intrinsic value of resources by cyclically using and utilizing limited resources around the world. Originality/value - It aims to contribute to the growth of consumer value-related industries and the welfare of society by providing implications from the point of view of sharing platform services.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.17-32
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• 2022

This study aimed to examine the effect of ad attributes on engagement, the mediating effect of engagement on the relationship between ad attributes and advertising effectiveness (attitudes toward ads, continuous intention to search, and e-WOM intention), and the differences in advertising effectiveness at different levels (low vs. high) of curiosity toward fashion video ads in the online context. For this purpose, a total of 408 responses were collected from consumers who were aged 20-40 years and had viewed fashion video ads via PC/mobile channels in the preceding six months. The results showed that three advertising attributes, namely informativeness, entertainment, and personalization, were significant predictors of engagement. Additionally, engagement had a significant mediating effect on the relationship between entertainment and ad effectiveness. Moreover, both informativeness and entertainment had a significant direct effect on the behavioral intention to search and engage in e-WOM. At the high-curiosity level, engagement had a significant influence on ad attitudes and e-WOM intention. In contrast, at the low-curiosity level, entertainment had a significant influence on e-WOM intention and continuous intention to search. These findings are meaningful in that they extend the advertising attitude model to fashion video ads in the online context.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.288-294
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• 2022

The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

• The Korean Journal of Food & Health Convergence
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• v.9 no.4
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• pp.19-23
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• 2023

The integration of ChatGPT, an AI-powered language model, is causing a profound transformation within the food industry, impacting various domains. It offers novel capabilities in recipe creation, personalized dining, menu development, food safety, customer service, and culinary education. ChatGPT's vast culinary dataset analysis aids chefs in pushing flavor boundaries through innovative ingredient combinations. Its personalization potential caters to dietary preferences and cultural nuances, democratizing culinary knowledge. It functions as a virtual mentor, empowering enthusiasts to experiment creatively. For personalized dining, ChatGPT's language understanding enables customer interaction, dish recommendations based on preferences. In menu development, data-driven insights identify culinary trends, guiding chefs in crafting menus aligned with evolving tastes. It suggests inventive ingredient pairings, fostering innovation and inclusivity. AI-driven data analysis contributes to quality control, ensuring consistent taste and texture. Food writing and marketing benefit from ChatGPT's content generation, adapting to diverse strategies and consumer preferences. AI-powered chatbots revolutionize customer service, improving ordering experiences, and post-purchase engagement. In culinary education, ChatGPT acts as a virtual mentor, guiding learners through techniques and history. In food safety, data analysis prevents contamination and ensures compliance. Overall, ChatGPT reshapes the industry by uniting AI's analytics with culinary expertise, enhancing innovation, inclusivity, and efficiency in gastronomy.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.1-25
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• 2024

Consumer demand for e-commerce services has skyrocketed due to the introduction of social distancing standards and lockdown measures that countries have taken to combat the pandemic. There has been a notable surge in the popularity of on-demand delivery services, with a significant influx of new users turning to the e-platform for assistance. This research paper tries to identify the enablers of E-commerce Utility and Service Quality and establish a connection using total interpretive structural modelling (TISM). Enablers are the building blocks for providing customers with an enhanced and more consistent service experience contributing to service quality. The enablers and the linkages thus established hold valuable insights for e-commerce marketers, aiding them in effectively reaching their customers, and achieving desired growth outcomes. The TISM- based model and the MICMAC analysis identified two barriers; website design and personalization as the decisive attributes of e-commerce service quality, possessing strong driving power and weak dependence. Furthermore, the factors of reliability, responsiveness, information, and ease of use form the linkage zone, indicating that any action taken on these factors would not only influence other factors but also have a reciprocal effect on them.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.45-69
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• 2024

Purpose - To examine the influence of mobile visual search as a strategic technology service on consumer perceived economic value and customer commitments, which in turn affect consumer's usage intention of mobile visual search. This study also explores the moderating effect of different levels of consumer online shopping orientation. Design/methodology/approach - One-by-one open-ended in-depth interview was first undertaken to 15 Korean consumers to figure the features of mobile visual search. Then a conceptual model was built to verify the hypotheses that indicate the impact of mobile visual search on consumer perceived economic value and customer commitment, which further influence consumer's usage intention. Findings - The results show Convenience, Information quality, Personalization, Text-free search interface design and Visual communication of mobile visual search positively influence consumer perceived economic value and customer commitment and in turn positively affect consumer's usage intention. Moreover, the different levels of consumer online shopping orientation also found to have different effects on consumers' perception and behavior of using mobile visual search in online fashion shopping. Research implications or Originality - The present study verified that mobile visual search is a service tool that consumers want to use in the online fashion shopping journey since it provides economic benefits.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.1-10
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• 2013

Personalization aims to provide customized contents to each user by using the user's personal preferences. In this sense, the core parts of personalization are regarded as recommendation technologies, which can recommend the proper contents or products to each user according to his/her preference. Prior studies have proposed novel recommendation technologies because they recognized the importance of recommender systems. Among several recommendation technologies, collaborative filtering (CF) has been actively studied and applied in real-world applications. The CF, however, often suffers sparsity or scalability problems. Prior research also recognized the importance of these two problems and therefore proposed many solutions. Many prior studies, however, suffered from problems, such as requiring additional time and cost for solving the limitations by utilizing additional information from other sources besides the existing user-item matrix. This study proposes a novel implicit rating approach for collaborative filtering in order to mitigate the sparsity problem as well as to enhance the performance of recommender systems. In this study, we propose the methods of reducing the sparsity problem through supplementing the user-item matrix based on the implicit rating approach, which measures the trust level among users via the existing user-item matrix. This study provides the preliminary experimental results for testing the usefulness of the proposed model.

• Knowledge Management Research
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• v.22 no.3
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• pp.235-252
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• 2021

The development of information and communication technology and COVID-19 have caused an unusual change in the hotel industry, and the demand for the contactless services such as service robots from hotel customers has surged. Therefore, this study investigates the perception of hotel customers on contactless services by applying a mixed-method analysis. Specifically, this study identified the causal correlations between variables through the structural equation model, and further applied the fuzzy set qualitative comparison analysis to derive patterns of variables that form the intention to use the non-face-to-face services. As a result of the analysis, it was shown that service experience co-creation, palyfulness, personalization, and trust had a significant effect on intention to use through the contactless service use desire. On the other hand, in the results of fuzzy-set qualitative comparison analysis, playfulness was derived as a core factor in all patterns. Based on these analysis results, this study provides academic basis for in-depth understanding of hotel customers' perception of contactless service and specific guidelines for hotel managers on the contactless service strategies in the era of COVID-19 pandemic.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.47-56
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• 2015

Automated detection of interesting web pages could be used in many different application domains. Determining a user's interesting web pages can be performed implicitly by observing the user's behavior. The task of distinguishing interesting web pages belongs to a classification problem, and we choose white box learning methods (fixed effect logit regression and support vector machine) to test empirically. The result indicated that (1) fixed effect logit regression, fixed effect SVMs with both polynomial and radial basis kernels showed higher performance than the linear kernel model, (2) a personalization is a critical issue for improving the performance of a model, (3) when asking a user explicit grading of web pages, the scale could be as simple as yes/no answer, (4) every second the duration in a web page increases, the ratio of the probability to be interesting increased 1.004 times, but the number of scrollbar clicks (p=0.56) and the number of mouse clicks (p=0.36) did not have statistically significant relations with the interest.