• Journal of the Ergonomics Society of Korea
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• v.27 no.4
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• pp.85-94
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• 2008

Internet shopping has been getting more rousing due to extension of supply with PC(personal computer) and a rapid rise of use of internet. Some companies have been continually researching in how to serve individuals with each ordered information, which aimed at getting ordinary customers to induce to be loyal customers. For that, there is progress of a service of a web-recommendation which considers individual attribution. This study is suggested a method which is a service of the web-recommendation by access to sensibility ergonomics approach. Previous studies established that service had a weak point. It did not manage to realize new needs of customers. Proposed service of the web-recommendation has been designed, which preferentially propose goods included customer's sensibility to the customer who wants it. This study is expected that it will encourage a rise of products' purchasing power of customers, make an increase in a profit of both sellers and people who operate electric commercial and satisfaction of customers will go up in the same. Also, products accord with sensibility of customers will be recommended customers by the suggested service of the web-recommendation. In addition, there will be a decline of time-consuming about making a choice among some products.

• Journal of Information Technology Applications and Management
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• v.26 no.5
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• pp.57-65
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• 2019

Recommendation Systems are information technologies that E-commerce merchants have adopted so that online shoppers can receive suggestions on items that might be interesting or complementing to their purchased items. These systems stipulate valuable assistance to the user's purchasing decisions, and provide quality of push service. Traditionally, Recommendation Systems have been designed using a centralized system, but information service is growing vast with a rapid and strong scalability. The next generation of information technology such as Cloud Computing and Big Data Environment has handled massive data and is able to support enormous processing power. Nevertheless, analytic technologies are lacking the different capabilities when processing big data. Accordingly, we are trying to design a conceptual service model with a proposed new algorithm and user adaptation on dynamic recommendation service for big data environment.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.55-60
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• 2020

Adaptive recommendation systems have been developed with big data processing as a system that provides services tailored to users based on user information and usage patterns. Deep learning can be used in these adaptive recommendation systems to handle big data, providing more efficient user-friendly recommendation services. In this paper, we propose a system that uses deep learning to categorize and recommend tourism types to suit the user's personality. The system was divided into three layers according to its core role to increase efficiency and facilitate maintenance. Each layer consists of the Service Provisioning Layer that real users encounter, the Recommendation Service Layer, which provides recommended services based on user information entered, and the Adaptive Definition Layer, which learns the types of tourism suitable for personality types. The proposed system is highly scalable because it provides services using deep learning, and the adaptive recommendation system connects the user's personality type and tourism type to deliver the data to the user in a flexible manner.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.322-329
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• 2017

Recently, the importance of personalized healthcare(wellness) services is increasing in the era of the 4th Industrial Revolution. However, the authoring of wellness contents fused with variety of contents and the study of the system which provides the customized recommendation are insufficient. In this paper, we proposes the recommendation service system for the customized convergence wellness contents. The proposed system makes to the wellness contents by the existing cultural/tourism/leisure contents and recommends the customized wellness contents based on a user's profile and the situation information such as location and weather. The proposed systems is expected to contribute to designing the innovative and new service models for the tailored wellness content.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.592-600
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• 2004

A recommendation system, which is an application area of e-CRM in e-commerce environment, provides individualized goods recommendation service that meets the demand of individual users. In general, existing recommendation systems require extensive historic user information in application domains. However, the method of recommendation based on static historic user information needs to respond flexibly to users'demand that changes rapidly and sensitively over time and in domains including a variety of users. In addition, it is difficult to recommend for new users who are not fall into any of existing domains. To overcome such limitations and provide flexible recommendation service, this study designed and implemented CPAR (Customer Preference Analysis Recommender) system that supports customer preference analysis and filter management. The filtering management capacity of the present system eases the necessity of extensive information about new users. In addition, CPAR system was implemented in XML-based wireless Internet environment for recommendation service independent from platforms and not limited by time and place.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.110-115
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• 2006

In pure P2P environment, it is possible to provide service by using a little real-time information without using accumulated information. But in case of using only a little information that was locally collected, quality of recommendation service can be fallen-off. Therefore, it is necessary to study a method to improve qualify of recommendation service by using users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information per each service field and classifying it per each user, using SOM. In addition, we could recommend proper services for users by quantifying the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.112-120
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• 2011

This research aims to show that the context adaptation system can be used to develop practical context-aware applications by developing a restaurant recommendation system based on location-awareness. In this research, we have designed and implemented a location-aware restaurant recommendation system which provides a customized restaurant recommendation service based on the user's current context. The context-adaptation engine adapts the application program according to the policy file as contexts are changed, and the application provides restaurant recommendation service based on the changed context like location.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.183-191
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• 2017

In order to secure the convenience of information retrieval by users of scientific information service platforms and to reduce the time required to acquire the proper information, this study proposes an optimized content recommendation algorithm among the algorithms that currently provide service menus and content information for each service, and conducts comparative evaluation on the results. To enhance the recommendation accuracy, users' major items were added to the original algorithm, and performance evaluations on the recommendation results from the original and optimized algorithms were performed. As a result of this evaluation, we found that the relevance of the content provided to the users through the optimized algorithm was increased by 21.2%. This study proposes a method to shorten the information acquisition time and extend the life cycle of the results as valuable information by automatically computing and providing content suitable for users in the system for each service menu.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.33-43
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• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.