• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.428-438
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• 2013

In the highly competitive coffee market, each coffee shop is striving to improve customer loyalty by providing a high level of service quality. To deepen our understanding of service quality in the coffee shop market, this study identifies the key elements of service quality of coffee shops and investigates their impacts on decision-making processes of female university students. This study also investigates the effects of customer satisfaction and brand image on customer loyalty in a coffee shop market. Moreover, it considers the two critical customer loyalty: repurchasing intention and recommendation intention. Data collected from 206 female university students were empirically tested against a research model using partial least squares. Analysis results showed that service product and service delivery significantly affect customer satisfaction and brand image whereas service intangible and service environment do not significantly influence on them. Customer satisfaction and brand image play an important role on the formation of repurchasing and recommendation intention.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.123-135
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• 2020

This study examined how the quality of Netflix service affects user satisfaction and continuance intention. This study classified the quality of Netflix service as content diversity, rate system appropriateness, recommendation system, N-screen service, binge viewing, and service quality, and examined the effect of each dimension on user satisfaction and continuous intention. We conduced an online survey on 202 Netflix users and analyzed the data with the SEM. Results are as follows. First, content diversity, recommendation system, binge-viewing and service quality are positively associated with user satisfaction. Second, the N-Screen service has neither direct nor indirect effects on continuance intention. However, rate system has a direct effect on continuance intention. On the other hand, content diversity, recommendation systems, binge-viewing, and quality of service affect continuance intention positively through user satisfaction. Finally, it is shown that user satisfaction and continuance intention have a significant static correlation as predicted.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.99-105
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• 2021

In addition to the OTT video production service represented by Nexflix and YouTube, a personalized recommendation system for content with artificial intelligence has become common. YouTube's personalized recommendation service system consists of two neural networks, one neural network consisting of a recommendation candidate generation model and the other consisting of a ranking network. Netflix's video recommendation system consists of two data classification systems, divided into content-based filtering and collaborative filtering. As the online platform-led content production is activated by the Corona Pandemic, the field of virtual influencers using artificial intelligence is emerging. Virtual influencers are produced with GAN (Generative Adversarial Networks) artificial intelligence, and are unsupervised learning algorithms in which two opposing systems compete with each other. This study also researched the possibility of developing AI platform based on individual recommendation and virtual influencer (metabus) as a core content of OTT in the future.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.389-402
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• 2015

Due to tough competition among social network services, technological specification alone could not be an adoption factor by the users. Instead, emotional factors such as a brand image and feeling towards an SNS brand became important factors in service differentiation. This study examined Korean young users perception of brand personalities of three social network services, Facebook, Kakao Story, and Band. It also analyzed the influence of the perception of brand personality, brand-self-image congruence, and brand affect on brand recommendation to others. The authors conducted a survey of Korean college students. The results indicate that SNS users perceived three SNS's brand personalities differently, and the positive perception of an SNS service has a positive effect on brand recommendation. Brand personality, brand-self-image congruence, and brand affect combined determine brand recommendation. When the brand personality variable is statistically controlled, brand affect has strong effect on brand recommendation.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.410-422
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• 2009

The objectives of the study were to investigate the influence of exhibition service attributes on customers' satisfaction and behavior intention. A method of this study was a survey research. The samples selected were 430 exhibition visitors to KINTEX for 2007 Seoul Motor Show. According to the results of this study, first of all, there is a significant relationship between information service and customer's satisfaction. Second, there is a significant relationship between convenience service and customer's satisfaction. Third, there is a significant relationship between promotion service and visitor's satisfaction to exhibition. Forth, there is a significant relationship between Customer's satisfaction and revisit intention. Fifth, there is a significant relationship between visitor's satisfaction to exhibition and recommendation intention. The result of this study will be helpful for the exhibition industries, giving needs to establish the marketing strategies.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.501-518
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• 2017

Reducing the time it takes for researchers to acquire knowledge and introduce them into research activities can be regarded as an indispensable factor in improving the productivity of research. The purpose of this research is to cluster the information usage patterns of KOSEN users and to suggest optimization method of personalized recommendation service algorithm for grouped users. Based on user research activities and usage information, after identifying appropriate services and contents, we applied a Spark based big data analysis technology to derive a personal recommendation algorithm. Individual recommendation algorithms can save time to search for user information and can help to find appropriate information.

• ETRI Journal
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• v.29 no.3
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• pp.311-321
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• 2007

In this paper, we propose a user-centric conflict management method for media services which exploits personal companions for the harmonious detection and resolution of service conflicts. To detect conflicts based on the varying characteristics of individual users, the proposed method exploits the unified context describing all users attempting to access media services. It recommends and mediates users' preferred media contents through a shared screen and personal companions to resolve the detected conflicts. During the recommendation, a list of preferred media contents is displayed on the shared screen, and a personally preferred content list is shown on the user's personal companion comprising the selection of media contents. Mediation assists the selection of a consensual service by gathering the users' selections and highlighting the common media contents. In experiments carried out in a ubiHome, we observed that recommendations and mediation are useful in harmoniously resolving conflicts by encouraging user participation in conflict situations.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.133-141
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• 2015

Mass multimedia contents are shared through various social media servies including social network service. As social network reveals user's current situation and interest, highly satisfactory personalized recommendation can be made when such features are applied to the recommendation system. In addition, classifying the music by emotion and using analyzed information about user's recent emotion or current situation by analyzing user's social network, it will be useful upon recommending music to the user. In this paper, we propose a music recommendation method that makes an emotion model to classify the music, classifies the music according to the emotion model, and extracts user's current emotional state represented on the social network to recommend music, and evaluates the validity of our method through experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.300-301
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• 2016

This paper proposes customized recommendation algorithm to improve the QoS(quality of service) of sport for all sports content uses to user profile and team grade. The proposed recommendation module is based on user profile information, and it recommends suitable team contents to user with Euclidean distance algorithm and preference weights between teams.