• 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.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.541-572
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• 2023

Amazon Go is a cashierless convenience store concept, which is seen as a disruption in the grocery retail segment. Although Amazon Go has the ability to disrupt the retail segment, there are speculations on how Amazon Go will be perceived by users. Existing studies have not utilized user-generated content to understand the factors that affect customer behaviour in case of Amazon Go. Additionally, in case of phygital retail, studies have not attempted at understanding the effect of emotions and gratifications on user behaviour. To address the gap of exploring user perspectives based on their experience, we have examined the impact of gratifications and emotions on the acceptance of phygital retail using user-generated-content. A mixed-method approach has been utilized using only user-generated content. Utilizing topic-modelling based content analysis and emotion analysis on 30 articles related to Amazon Go, we found themes like, convenience, technology, experience, personalization, enjoyment and emotions like, bad, good, annoyance, success. In the empirical analysis, we have utilized 522 reviews about Amazon Go from the cognition and emotion theory stance, and found that hedonic gratifications have a positive impact on challenge emotions. We also found a significant impact of emotions on customer's favourite behaviour.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2004.10a
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• pp.259-279
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• 2004

[ $\box$ ] 방송통신 융합의 가속화 $\box$ 통방융합의 Business Models $\box$ 이종망을 통한 다양한 단말에서의 콘텐츠 서비스 $\blacksquare$ Ubiquitous Content Access - Anytime, Anywhere, Any Contene $\box$ 멀티미디어 프레임워크 필수적 - 상호연도 가능한 콘텐츠 생성/보호, 전달, 적응/소비 제공 $\box$ 통방융합 멀티미디어 프레임워크 기술 개발 추진 $\box$ MPEG-21 Multimedia Framework에 기반한 핵심기술 개발 $\blacksquare$ Content Adaptation & Personalization - MPEG-21(DIA), MPEG-7, TV-Anytime Forum $\blacksquare$ End-to-end Qos $\blacksquare$ Scalable Video Coding & Delivery, etc $\box$ UMA 기술 $\box$ EU Project : ENTHRONE

• International Journal of Knowledge Content Development & Technology
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• v.4 no.2
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• pp.25-47
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• 2014

The goal of the present study is to explore IM interruption by relating it to media choices and purposes of IM use in the workplace. Two major media choice concepts were: media richness and social influence; while four purposes of IM use were: organization work, knowledge work, socializing, and boundary spanning activities. Data (N = 283) were collected via a combination of convenience and snowball sampling of "computer-using workers" in Taiwan, based on the Standard Occupational Classification system published by the Taiwan government. Results indicated that media choice works better than purpose of IM use to explain IM interruption. Among them, social influence was the best predictor to IM interruption in the workplace. In addition, instant feedback and personalization provided by IM, and IM usage for the purposes of knowledge work and socializing, also relate to IM interruption in the workplace.

• Journal of Multimedia Information System
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• v.4 no.3
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• pp.123-144
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• 2017

Personalized media includes user-targeted and user-generated content (UGC) exchanged through social media and interactive applications. The increased consumption of UGC presents challenges and opportunities to multimedia information systems. We work towards modeling a deep structure for content networks. To gain insights, a hybrid practice with Media Framework (MF) is presented for network creation of personalized media, which leverages the authoring methodology with user-generated semantics. The system's vertical integration allows users to audition their personalized media networks in the context of a global system network. A navigation scheme with dynamic GUI shifts the interaction paradigm for content query and sharing. MF adopts a multimodal architecture anticipating emerging use cases and genres. To model diversification of platforms, information processing is robust across multiple technology configurations. Physical and virtual networks are integrated with distributed services and transactions, IoT, and semantic networks representing media content. MF applies spatiotemporal and semantic signal processing to differentiate action responsiveness and information responsiveness. The extension of multimedia information processing into authoring enables generating interactive and impermanent media on computationally enabled devices. The outcome of this integrated approach with presented methodologies demonstrates a paradigmatic shift of the concept of UGC as personalized media network, which is dynamical and evolvable.

• 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.

• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.167-183
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• 2006

This paper describes a metadata extraction technique based on natural language processing (NLP) which extracts personalized information from email communications between financial analysts and their clients. Personalized means connecting users with content in a personally meaningful way to create, grow, and retain online relationships. Personalization often results in the creation of user profiles that store individuals' preferences regarding goods or services offered by various e-commerce merchants. We developed an automatic metadata extraction system designed to process textual data such as emails, discussion group postings, or chat group transcriptions. The focus of this paper is the recognition of emotional contents such as mood and urgency, which are embedded in the business communications, as metadata.

• ETRI Journal
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• v.42 no.2
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• pp.272-281
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• 2020

Three-dimensional (3D) selfie services, one of the major 3D printing services, print 3D models of an individual's face via scanning. However, most of these services require expensive full-color supporting 3D printers. The high cost of such printers poses a challenge in launching a variety of 3D printing application services. This paper presents a stencil-based 3D facial relief creation method employing a low-cost RGBD sensor and a 3D printer. Stencil-based 3D facial relief is an artwork in which some parts are holes, similar to that in a stencil, and other parts stand out, as in a relief. The proposed method creates a new type of relief by combining the existing stencil techniques and relief techniques. As a result, the 3D printed product resembles a two-colored object rather than a one-colored object even when a monochrome 3D printer is used. Unlike existing personalization-based 3D printing services, the proposed method enables the printing and delivery of products to customers in a short period of time. Experimental results reveal that, compared to existing 3D selfie products printed by monochrome 3D printers, our products have a higher degree of similarity and are more profitable.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.105-124
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• 2022

Purpose: The purpose of this study is to examine the factors that influence the pre-use expectation and the continuous usage intention of the OTT services. Methods: For empirical analysis, the survey for this study was administered with many anonymous people who had previously used any of the OTT services and valid 192 data were analyzed by SPSS and PLS-SEM. Results: The results of this study are as follows. Personalization and understandability in the information quality didn't significantly affect confirmation. Ease of use and search of the system quality significantly affected confirmation. Also, it was found that content diversity, content recency, and content playfulness as the characteristics of the content quality significantly affected the expectation confirmation. OTT service fees also significantly affected the expectation confirmation. It also significantly affected perceived usefulness and satisfaction. Finally, satisfaction positively influenced the continuouse intention to use. Conclusion: The findings of this empirical analysis shows that the specific characteristics related to the relationship of expectation confirmation, perceived usefulness, satisfaction, and continuous usage intention with respect to OTT services through the Post Acceptance Model (PAM). Because system quality, content quality, and service fees meeted users' expectations, OTT services need a strategy that can boost the users' positive perceptions or experiences by reinforcing these three factors.

• CRM연구
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• v.4 no.1
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• pp.55-68
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• 2011

Recently with the development of Web services by utilizing a variety of web content, the studies on user experience and personalization based on web usage has attracted much attention. Majority of personalized analysis are have been carried out based on existing data, primarily using the database and statistical models. These approaches are difficult to reflect in a timely mannerm, and are limited to reflect the true behavioral characteristics because the data itself was just a result of customers' behaviors. However, recent studies and commercial products on web analytics try to track and analyze all of the actions from landing to exit to provide personalized service. In this study, by analyzing the customer's click-stream behaviors, we define U-Score(Usage Score), P-Score (Preference Score), M-Score(Mania Score) to indicate variety of customer preferences. With the devised three indicators, we can identify the customer's preferences more precisely, provide in-depth customer reports and customer relationship management, and utilize personalized recommender services.