• Journal of Information Technology Applications and Management
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• 제24권1호
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• pp.111-128
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• 2017

Existing studies for recommender have focused on recommending an appropriate item based on the customer preference. However, it has not yet been studied actively to recommend purchase timing for the repurchase product despite of its importance. This study aims to propose MLP and RNN models based on the only simple purchase history data to predict the timing of customer repurchase and compare performances in the perspective of prediction accuracy and quality. As an experiment result, RNN model showed outstanding performance compared to MLP model. The proposed model can be used to develop CRM system which can offer SMS or app based promotion to the customer at the right time. This model also can be used to increase sales for repurchase product business by balancing the level of order as well as inducing repurchase of customer.

• 상하수도학회지
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• 제17권4호
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• pp.510-518
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• 2003

The fluoride work management of water supply conveyance has been recommending by the WHO to prevent of tooth decay. Fluoridation of public water supplies has been practiced since 1945. The present approximately 67 countries reported community water fluoridation benefiting many cities. At our country, Fluoridation began in 1981 in Chongiu and Jinhea. In 2002, approximately 40 cities have large populations consuming fluoridated water. But Chongiu stopped fluoridation water works. Few public health measures have been accorded greater clinical and laboratory research, epidemiological study, clinical trials, and public attention than has water fluoridation. In this study, chemical analysis of Sodium Silicofluoride and Fluoride Feed Equipment analyzed. And this study proposed to Fluoride concentration experimental (lab. and field exp.), economics analysis, prevention effect. This study can be decided on the concentration of fluoride injection in Water Fluoridation. Hereafter, this study will be useful in safety and economics of Water Fluoridation in the future.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2004년도 추계학술대회 및 정기총회
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• pp.430-460
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• 2004

This study presented a personalization system that adopted a methodology which is applicable for digital content recommendation and executed by the Internet service providers. The system made a recommendation to the users on the basis of their preferences, while most techniques for recommending digital content have focused on considering the similarity of content. In addition, it developed a method of evaluation to determine the priority of recommendations and adopted measures when selecting a set of recommendations. To experiment the feasibility and effectiveness of the presented methodology, a prototype system was developed and was applied to an English newspaper on the Internet.

• 한국경영과학회지
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• 제40권4호
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• pp.123-144
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• 2015

Through the event study methodology and the case study on the Company T and its subsidiaries, this study analyzes the effect of credit rating downgrade in the Korean stock market. Our empirical results cast some doubts on whether credit rating agencies made adequate credit rating adjustments on the Chaebol companies, and suggest that little information was provided to the bond market investors. This study provides some policy implications by recommending that regulators encourage credit rating agencies to provide more accurate and appropriate information to market participants.

• Journal of Information Science Theory and Practice
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• 제2권2호
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• pp.33-47
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• 2014

Mobile devices are the most important equipment for accessing various kinds of services. These services are accessed using wireless signals, the same used for mobile calls. Today mobile services provide a fast and excellent way to access all kinds of information via mobile phones. Mobile service providers are interested to know the access behavior pattern of the users from different locations at different timings. In this paper, we have introduced an associated tree for analyzing user behavior patterns while moving from one location to another. We have used four different parameters, namely user, location, dwell time, and services. These parameters provide stronger frequent accessing patterns by matching joins. These generated patterns are valuable for improving web services, recommending new services, and predicting useful services for individuals or groups of users. In addition, an experimental evaluation has been conducted on simulated data. Finally, performance of the proposed approach has been measured in terms of efficiency and scalability. The proposed approach produces excellent results.

• 대한안전경영과학회지
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• 제10권4호
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• pp.309-314
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• 2008

Many studies on predicting and recommending information and products have been studying to meet customers' preference. Unnecessary information should be removed to satisfy customers' needs in massive information. The some information filtering methods to remove unnecessary information have been suggested but these methods have scarcity and scalability problems. Therefore, this paper explores a personalized recommendation system based on artificial neural network (ANN) to solve these problems. The insurance product recommendation is adapted as an example to demonstrate the proposed method. The proposed recommendation system is expected to recommended a suitable and personalized insurance products for customers' satisfaction.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2004년도 추계학술대회
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• pp.203-210
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• 2004

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems - content-based recommending and collaborative filtering. Collaborative filtering recommender systems have been very successful in both information filtering domains and e-commerce domains, and many researchers have presented variations of collaborative filtering to increase its performance. However, the current research on recommendation has paid little attention to the use of time related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest. This paper proposes dynamic fuzzy clustering algorithm and apply it to collaborative filtering algorithm for dynamic recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes. The results of the evaluation experiment show the proposed model's improvement in making recommendations.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2013년도 제25회 한글 및 한국어 정보처리 학술대회
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• pp.107-109
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• 2013

주식 시장에서 거래되고 있는 증권은 MACD(Moving Average Convergence Divergence), Stochastic 등의 보조 지표를 이용하는 기술적 분석을 통하여 매수/매도 시점을 결정한다. 주식 시장의 객관적인 자료를 통하여 분석하는 기술적 분석 방법은 주식 시장 외적인 요소를 반영하는데 있어 한계점이 존재한다. 본 논문에서는 기술적 분석 방법에 기사를 종목별로 분류하고 기사의 긍정 및 부정을 판별하는 문서 분류 기법을 적용하여 주식 외적인 요소를 반영하는 시스템을 제안한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 추계학술발표대회
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• pp.1042-1045
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• 2014

Recently several recommender systems have been developed in a variety of applications, but providing accurate recommendations that match the preferences and constraints of various users is quite challenging. This paper presents a method of recommending digital products based on the past preference of a user on the price, rating and selling volume of a product. Experimental results of the method with actual data of Amazon showed that the average accuracy of the recommendations made by the method is 85%. Although the results are preliminary, the method is potentially capable of making more accurate personalized recommendations than existing methods.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 추계학술대회
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• pp.602-609
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• 2007

Combining collaborative filtering with some other technique is most common in hybrid recommender systems. As many recommended items from collaborative filtering seem to be similar with respect to content, the collaborative-content hybrid system suffers in terms of quality recommendation and recommending new items as well. To alleviate such problem, we have developed a novel method that uses a diversity metric to select the dissimilar items among the recommended items from collaborative filtering, which together with the input when fed into content space let us improve and include new items in the recommendation. We present experimental results on movielens dataset that shows how our approach performs better than simple content-based system and naive hybrid system