• The Journal of Society for e-Business Studies
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• v.9 no.3
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• pp.191-205
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• 2004

In banking industry, as competition of retail finance is intensive, there appeared eCRM which is a kind of marketing strategy, not only screening good customers but also doing one-to-one marketing for improving customer royalty and satisfaction of goods and service of bank. The strategic importance of eCRM is increasing more and more. We studied bank to use the advantage of expert system and eCRM. The expert system is used as a tool to analyze eCRM. In this paper, first, we presented an eCRM system architecture using expert systems in banking business. Secondly, a bank financial goods recommendation system using expert system is designed and developed. Customers will receive the good quality financial services from the expert system and banks also will acquire the good quality customer data from the expert system.

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.51-58
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• 2010

Programming education needs learning which is adjusted individual learners' level of their learning abilities. Recommendation system is one way of implementing personalized service. In this research, we propose recommendation method which learning items are recommended for individual learners' learning in web-based programming education environment by. Our proposed system for leveled programming education provides appropriate programming problems for a certain learner in his learning level and learning scope employing collaborative filtering method using learners' profile of their level and correlation profile between learning topics. As a result, it resolves a problem that providing appropriate programming problems in learner's level, and we get a result that improving leaner's programming ability. Furthermore, when we compared our proposed method and original collaborative filtering method, our proposed method provides the ways to solve the scalability which is one of the limitations in recommendation systems by improving recommendation performance and reducing analysis time.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.224-230
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• 2021

YouTube is an OTT service that leads the home economy, which has emerged from the 2020 Corona Pandemic. With the growth of OTT-based individual media, creators are required to establish attractive storytelling strategies that can be preferred by viewers and elected for YouTube recommendation algorithms. In this study, we conducted a study on modeling that proposes a content storyline for creators. As the ability for Creators to create content that viewers prefer, we have presented the data literacy ability to find patterns in complex and massive data. We also studied the importance of compelling storytelling configurations that viewers prefer and can be selected for YouTube recommendation algorithms. This study is of great significance in that it deviated from the viewer-oriented recommendation system method and proposed a story suggestion model for individual creaters. As a result of incorporating this story proposal model into the production of the YouTube channel Tiger Love video, it showed a certain effectiveness. This story suggestion model is a machine learning text-based story suggestion system, excluding the application of photography or video.

• 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.

• Journal of Information Technology and Architecture
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• v.10 no.2
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• pp.251-261
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• 2013

The appearance of various mobile Internet environment access to existing networks of mobile devices is easier to tell. In addition, mobile device users to use the wireless environment than a wired environment, user profile information is readily available features. Mobile devices have features that use alone. These characteristics of mobile devices to apply the personalization service is the best system. This paper proposes for mobile device users a personalized mobile music content recommendation service. This service propose to utilizes the user's access history information using time-weighting and collaborative filtering. Access history information can find out information of user interest. Using this information, consider the preference of music genre and time-weighted based on the recommendations makes the music. This method the problem of the traditional music recommendation system, point user's favorite music genre is changing over time do not consider that to solve the problem.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1387-1392
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• 2015

SCS(Social Curation Service) is a service system to help sale and consumption with intelligent information about consumer's favor which is got from the combination of social service and internet shopping mall. This paper develops and analyzes some algorithms for catching the customer's preference tendency in SCS system. The developed algorithms are implemented to verify it's efficiency.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.292-302
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• 2007

Thousands of studies on predicting information and products that are suitable for customers' preference have been actively proceeding. In massive information, unnecessary information should be removed to satisfy customers' needs. This Information filtering has been proceeding with several methods such as content-based and collaborative filtering etc. These conventional filtering methods have scarcity and scalability problems. Thus, this paper proposes a recommendation system using BPN to solve them. Data obtained by survey questionnaire are used as training data of neural network. The recommendation system using neural network is expected to recommend suitable products because it creates optimal network. Finally, the prototype for recommendation system based on neural network is proposed to collect data and recommend appropriate methods through survey questionnaire. As a result, this research improved the problems of conventional information filtering.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2434-2441
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• 2015

This paper proposes an exercise recommendation system for treating obesity that provides systematic recommendations for exercise and diet. Five body indices are considered as indicators for recommend exercise and diet. The system also informs users of prohibited foods using health data including blood pressure, blood sugar, and total cholesterol. To maximize the utility of the system, it displays recommendations for both indoor and outdoor activities. The system is equipped with multimode sensors, including a three-axis accelerometer, a laser, a pressure sensor, and a wrist-mounted sensor. To demonstrate the effectiveness of the system, field tests are carried out with three participants over 20 days, which show that the proposed system is effective in treating obesity.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1075-1087
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• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.