• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.911-923
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• 2014

This study explores which organizational capital is important for the customer service center and how the organizational capital is linked to organization's performance through dynamic capability. In this study, total of 389 employees in customer service centers were surveyed for the analysis. The results indicate that relational capital and organizational culture were positively linked with collaborative behavior, capital share, and capital transformation. Also, structural capital was a significant factor in collaborative behavior. Organizational performance was positively affecting collaborative behavior and capital share. This study provides a practical guideline on how to manage organizational capital and supplement shortcomings for managers and counsellors at the customer service centers. Furthermore, the implications for the reinforcement and development of organizational capital were suggested in building a customer service center as a strategic and fundamental part of the company.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.135-146
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• 2010

In this paper, we propose an Recommendation System for supporting self-directed learning on e-learning marketplace. The key idea of this system is recommendation system using revised collaborative filtering to support marketplace. Exisiting collaborative filtering method consists of 3 stages as preparing low data, building familiar customer group by selecting nearest neighbor, creating recommendation list. This study designs recommendation system to support self-directed learning by using collaborative filtering added nearest neighbor learning course that considered industry and learning level. This service helps to select right learning course to learner in industry. Recommendation System can be built by many method and to recommend the service content including explicit properties using revised collaborative filtering method can solve limitations in existing content recommendation.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.159-168
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• 2011

As e-commerce and social media service evolves, studies on recommender systems advance, especially concerning the application of collective intelligence to personalized custom service. With the development of smartphones and mobile environment, studies on customized service are accelerated despite physical limitations of mobile devices. A typical example is combined with location-based services. In this study, we propose a recommender system using movie genre similarity and preferred genres. A profile of movie genre similarity is generated and designed to provide related service in mobile experimental environment before prototyping and testing with data from MovieLens.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.346-348
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• 2022

Media Contents Recommendation Web Service (service name 'mobodra') is a web service that analyzes media types and genre tastes for each user and recommends content accordingly. Users select some of the works randomly provided on the web when signing up for membership and analyze their tastes based on this. Based on this analysis, preferred content for each user is recommended. In this paper, we implement a content recommendation algorithm through item-based collaborative filtering. When the user's activity data or preference is re-examined, the above process is executed again to update the user's taste.

• The Research Journal of the Costume Culture
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• v.19 no.3
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• pp.531-541
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• 2011

This study suggests basic data for the standardization of school uniform purchase by examining the statistics of purchasing practice school uniforms from information sources, purchasing methods, and consumer' perception about collaborative purchases. A survey was conducted with first grade male and female middle-school students, and their parents. A total of 344 questionnaires were returned and analyzed. The results were as follows: first, when making purchases, information sources were explained by parents, friends, senior students, or workers at uniform shops. The purchasing methods were popular brand uniforms or specialized uniform shops. Second, four factors were extracted from purchasing data for factor analysis. The factors were comfort, appearance, service, other external factors, and promotions. Third, the perception analysis and need of collaborative purchases were indicated by 90% of the students' parents, who were aware of collaborative purchase. Additionally, 71.2% answered collaborative purchase was necessary. Fourth, for future uniform purchases, 75.6% of the students answered to buy more popular brands, or products from specialized school uniform shops, while 54.4% of the parents answered positively to collaborative purchases. The results of the examination of consumer school uniform purchasing behavior will provide useful strategies for the standardization system for purchasing school uniforms.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Korean Journal of Computational Design and Engineering
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• v.9 no.3
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• pp.260-265
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• 2004

This paper proposed a Web-based training service for product data management by supporting an integrated product data management system, various technical documents. and efficient communication systems. It also supports a general product development process and a consistent product data model that enable participants to experience management of consistent product information during the product development life cycle. The Web based environment of the service also provides participants with a collaborative workplace with other participants and a Web portal for all the components of the service.

• Journal of The Korean Association of Information Education
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• v.11 no.3
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• pp.359-369
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• 2007

Model-centered instruction which presents expert mental model before or during learning can facilitate and help novice learners' problem solving process. During six phases of cognitive apprenticeship model which is a method for applying model centered instruction theory, collaborative learning strategy can maximize articulation and reflection of novice learners. This article presents results of a study that investigated the effects of model-centered instruction and collaborative learning on the learning of instructional design process for pre-service teachers attending a computer education class at an education university.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.4
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• pp.367-383
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• 2006

The purpose of this study is to develop a practical model for enhancing cooperative virtual reference services of public libraries in the nation. The research methods include an examination of model cases from Europe and the V.S. and also an electronic questionnaire survey of 375 public librarians (73% response rate). The study results suggest the need for 'a collaborative virtual reference room' which consists of the collaborative reference database, virtual reference desk, guidance and instruction designed by age groups and/or subjects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.