• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.58-64
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• 2005

Customer sensibility based recommendation agent system was developed to tailor to the customer the suggestion of goods and the description of store catalog in on-line shopping mall. The recommendation agent system composed of five modules and seven services including specialized algorithm. This study was to investigate the effectiveness of the customer sensibility based recommendation agent system in on-line shopping mall. This study asked 30 male and female students to perform the task in on-line shopping mall and facilitated them questionnaires. The questionnaires were administered to subjects to measure quality precision, ease of use, support of buying, purchasing power, future intention of the system. The study revealed that good part of the subjects positively evaluated the customer sensibility based recommendation system except for ease of use. The study on usability of the recommendation agent system has need to be performed in next. This paper shows that the satisfaction and the buying power of customers may be improved by presenting customer sensibility based recommendation in on-line shopping mall.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.1
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• pp.38-46
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• 2004

To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. This paper deals with an intelligent agent approach to incorporate customer's sensibility into an one-to-one recommendation service in on-line shopping mall. In this paper the focus of interest is on-line recommendation service algorithm for development of Human Sensibility based web agent system. The recommendation agent system composed of seven services including specialized algorithm. The on-line recommendation service algorithm use human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system's behavior requires the parallel execution of several tasks during the interaction (e.g., identifying the customer's emotional preference and dynamically generating the pages of the store catalog). Most of the present shopping malls go through the catalog of goods, but the future shopping malls will have the form of intelligent shopping malls by applying the on-line recommendation service algorithm.

• Journal of the Ergonomics Society of Korea
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• v.23 no.3
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• pp.1-12
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• 2004

In recent years, with the advent of e-Commerce the need for personalized services and one-to-one marketing has been emphasized. To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. In this paper, we provide an intelligent agent approach to incorporate human sensibility into an one-to-one recommendation service in cyber shopping mall. Our system exploits human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system`s behavior requires the parallel execution of several tasks during the interaction （e. g., identifying the customer`s emotional preference and dynamically generating the pages of the store catalog）. The recommendation agent system composed of five modules including specialized agents carries on these tasks. By presenting goods that are consistent with user interests as well as user sensibility, the accuracy and satisfaction of the recommendation service may be improved.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.60-66
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• 2016

Compound logistics is a service aimed to enhance logistics efficiency by supporting that shippers and consigners jointly use logistics facilities. Many of these services have taken place both domestically and internationally, but the joint logistics services for e-commerce have not been spread yet, since the number of the parcels that the consigners transact business is usually small. As one of meaningful ways to improve utilization of compound logistics, we propose a brokerage service for shipper and consigners based on the hybrid recommendation system using very well-known classification and clustering methods. The existing recommendation system has drawn a relatively low satisfaction as it brought about one-to-one matches between consignors and logistics vendors in that such matching constrains choice range of the users to one-to-one matching each other. However, the implemented hybrid recommendation system based brokerage agent service system can provide multiple choice options to mutual users with descending ranks, which is a result of the recommendation considering transaction preferences of the users. In addition, we applied feature selection methods in order to avoid inducing a meaningless large size recommendation model and reduce a simple model. Finally, we implemented the hybrid recommendation system based brokerage agent service system that shippers and consigners can join, which is the system having capability previously described functions such as feature selection and recommendation. As a result, it turns out that the proposed hybrid recommendation based brokerage service system showed the enhanced efficiency with respect to logistics management, compared to the existing one by reporting two round simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

• ETRI Journal
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• v.29 no.2
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.78-85
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• 2011

In this paper, a research on multi-agent is carried out in order to develop a system that can provide drivers with real-time route recommendation by reflecting Dynamic Environment Information which acts as an agent in charge of Driver's trait, road condition and Route recommendation system. DEI is equivalent to number of n multi-agent and is an environment variable which is used in route recommendation system with optimal routes for drivers. Route recommendation system which reflects DEI can be considered as a new field of topic in multi-agent research. The representative research of Multi-agent, the Prey Pursuit Problem, was used to generate a fresh solution. In this thesis paper, you will be able to find the effort of indulging the lack of Prey Pursuit Problem,, which ignored practicality. Compared to the experiment, it was provided a real practical experiment applying the algorithm, the new Ant-Q method, plus a comparison between the strategies of the established direction vector was put into effect. Together with these methods, the increase of the efficiency was able to be proved.

• The Journal of the Korea Contents Association
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• v.6 no.4
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• pp.55-62
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• 2006

Important issues emerging with the opening of the ubiquitous age are how to present ubiquitous environment and how services and access methods can be provided to users. The present research proposes a system that can provide users with useful information dynamically through intelligent multi agents in mobile environment. The system is composed of profile module, rule generation module, filtering module and service module. It was designed to find users' demands in an intelligent way based on information on users registered through the recommendation agent. We implemented an applied system and proved its performance through an experiment.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.343-350
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• 2020

In the era of the Fourth Industrial Revolution in the digital revolution is taking place, various attempts have been made to provide various contents in a digital environment. In this paper, agent-recruitment service system based on collaborative deep learning is proposed for the intelligent head hunting service. The service system is improved from previous research [7] using collaborative deep learning for more reliable recommendation results. The Collaborative deep learning is a hybrid recommendation algorithm using "Recurrent Neural Network(RNN)" specialized for exponential calculation, "collaborative filtering" which is traditional recommendation filtering methods, and "KNN-Clustering" for similar user analysis. The proposed service system can expect more reliable recommendation results than previous research and showed high satisfaction in user survey for verification.

• Journal of Convergence for Information Technology
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• v.9 no.2
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• pp.108-114
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• 2019

In this paper, we propose an intelligent vocabulary recommendation agent that automatically provides vocabulary corresponding to game-based learners' needs and requirements in the mobile education augmented reality game environment. The proposed agent reflects the characteristics of mobile technology and augmented reality technology as much as possible. In addition, this agent includes a vocabulary reasoning module, a single game vocabulary recommendation module, a battle game vocabulary recommendation module, a learning vocabulary list Module, and a thesaurus module. As a result, game-based learners' are generally satisfied. The precision of context vocabulary reasoning and thesaurus is 4.01 and 4.11, respectively, which shows that vocabulary related to situation of game-based learner is extracted. However, In the case of satisfaction, battle game vocabulary(3.86) is relatively low compared to single game vocabulary(3.94) because it recommends vocabulary that can be used jointly among recommendation vocabulary of individual learners.