• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.15-28
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• 2015

As personal devices and pervasive technologies for interacting with networked objects continue to proliferate, there is an unprecedented world of scattered pieces of contextualized information available. However, the explosive growth and variety of information ironically lead users and service providers to make poor decision. In this situation, recommender systems may be a valuable alternative for dealing with these information overload. But they failed to utilize various types of contextual information. In this study, we suggest a methodology for context-aware recommender systems based on the concept of contextual boundary. First, as we suggest contextual boundary-based profiling which reflects contextual data with proper interpretation and structure, we attempt to solve complexity problem in context-aware recommender systems. Second, in neighbor formation with contextual information, our methodology can be expected to solve sparsity and cold-start problem in traditional recommender systems. Finally, we suggest a methodology about context support score-based recommendation generation. Consequently, our methodology can be first step for expanding application of researches on recommender systems. Moreover, as we suggest a flexible model with consideration of new technological development, it will show high performance regardless of their domains. Therefore, we expect that marketers or service providers can easily adopt according to their technical support.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.285-286
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• 2016

We aim to propose the prediction modeling method of ship's position with extracting ship's trajectory model through pattern recognition based on the data that are being collected in VTS centers at real time. Support Vector Machine algorithm was used for data modeling. The optimal parameters are calculated with k-fold cross validation and grid search. We expect that the proposed modeling method could support VTS operators' decision making in case of complex encountering traffic situations.

• Journal of Korean Library and Information Science Society
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• v.54 no.2
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• pp.23-42
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• 2023

The library tour program is a new type of cultural program that was first introduced and operated by J City, and library tourists travel to specialized libraries in the city according to a set course and experience various experiences. This study aims to build a customized course recommendation model that considers the characteristics of individual participants in addition to the existing fixed group travel format so that more users can enjoy the opportunity to participate in library tours. To this end, the characteristics of library travelers were categorized to establish traveler personas, and library evaluation items and evaluation criteria were established accordingly. We selected 22 libraries targeted by the library travel program and measured library data through actual visits. Based on the collected data, we derived the characteristics of suitable libraries and developed a persona-based library tour course recommendation model using a decision tree algorithm. To demonstrate the feasibility of the proposed recommendation model, we build a mobile application mockup, and conducted user evaluations with actual library users to identify satisfaction and improvements to the developed model.

• Korean Journal of Construction Engineering and Management
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• v.11 no.6
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• pp.100-110
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• 2010

The main roots of CM service contracts include existing customer repurchases and those made by new customers by existing ones. The study on customers and loyalty can be factors to strengthen CM's competitiveness. However, there have been little attempt to study customer satisfaction and customer loyalty. Construction Management (CM), the advanced construction management method, was introduced 15 years ago in the mid 1990's in the domestic market. The aim of this research is to build a model that can predict customer loyalty based on how much customers are satisfied with CM service. To measure customer satisfaction and loyalty, this research surveyed 135 decision-makers who have experienced CM services. Customer satisfaction was tested and analyzed according to different phases: planning, designing, procurement, construction, and post construction. Referral intention was tested based on NPS theory. Customer types were divided into detractors, passively satisfied and promoters according to the tested measurement and multinomial logistic regression between the satisfaction by construction phases and customer types. This research resulted to a model that can predict customer types: detractors, passively satisfied and promoters, which were determined according to satisfaction level. The initial planning phase also revealed which factor is most influential for a customer to become promoter. These results can be used to acquire customer loyalty by managing the satisfaction of customers through a project under an Internet-based environment. Such can provide the needed information in quickly exploring positive and negative word-of-mouth feedbacks.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.693-703
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• 2005

Given the local traders with pattern-based multi-predictors of stock prices, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset allocated to each recommendation of the predictors, we design an asset allocation strategy called meta policy in the reinforcement teaming framework. We utilize both the information of each predictor's recommendations and the ratio of the stock fund over the total asset to efficiently describe the state space. The experimental results on Korean stock market show that the trading system with the proposed meta policy outperforms other systems with fixed asset allocation methods. This means that reinforcement learning can bring synergy effects to the decision making problem through exploiting supervised-learned predictors.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.395-401
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• 2014

As social network service becomes common, the consumers use many discount coupons with which they can purchase goods via social commerce. Although, the quantities of coupons offered from social commerce are currently on the sharp increase, customized coupon service that reflects user preference is not offered. This paper proposes a coupon service method reflecting user's subjective inclination targeting food coupons to offer customized coupon service for social commerce. Towards this end, this paper conducts hierarchization of the factors that become standard in selecting coupons including food types, food prices, discount rates and the number of buyers. And then, this study classifies, extracts and offers the coupons using Fuzzy-AHP, a decision making support method that reflects subjective inclination. From the user satisfaction results on the extracted coupons, the users are generally satisfied: very satisfactory with 45%, satisfactory with 33% and fair with 22%, and there was no experiment participant, who was dissatisfied.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.13-14
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• 2021

한국과학기술정보연구원(이하 KISTI)은 지난 11년 간 전사적으로 품질제고전략, 서비스만족전략, 이미지 제고전략 등 3대 고객만족 추진전략을 수립하여 체계적인 "고객만족경영시스템(CSM : Customer Satisfaction Management)"을 구축하고 이를 강화하기 위한 노력을 기울여 왔다. 본 연구의 목적은 과학기술정보서비스에 대한 고객만족도를 기반으로 하여 이용고객을 예측할 수 있는 모델을 구축하는 것이다. 이를 위해 과학기술정보 서비스를 경험한 500명의 의사결정자를 대상으로 과학기술정보서비스에 대한 고객충성도를 측정하였다. 고객의 충성도는 NPS(Net Promoter Score) 이론에 근거하여 하였다. 특히 연구결과 고객만족도 수준에 따라 비추천고객, 중립고객, 추천고객을 예측할 수 있는 모델을 구축하였다. 이와 같은 연구결과는 고객의 긍정적이거나 부정적인 구전으로 급속도로 노출되는 환경에서 고객의 만족도를 분석함으로써 기관의 주요 서비스별 고객을 확보하는데 사전 예측자료로 활용될 수 있다.

• Journal of Environmental Policy
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• v.7 no.1
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• pp.1-29
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• 2008

The Ministry of Environment(MoE) of Korea has recently established the Environmental Health Act. This Act contains a clause related to implementation of Health Impact Assessment(HIA). So, selecting a landfill which was expected to have an influence on human health among major development projects, this study carried out the human risk assessment due to inhalation exposure to landfill gas emission and attempted to measure the possibility of domestic application of HIA in the future. The process for HIA on landfill site extension focusing on human risk assessment is as follows: The first step is to presume and calculate the amount of landfill gas emissions using LandGEM, The second step is to carry out exposure assessment using K-SCREEN Model which is used for predicting the concentration in a conservative method. The last step is to carry out human risk assessment of carcinogenic and non-carcinogenic substances. It is considered that it is likely to apply a technique for human risk assessment due to inhalation exposure to landfill gas emission performed here more specifically in the case of implementing HIA. In addition, it is also believed that more systematic studies are needed to overcome some weak points and limits found in this study and if these weak points and limits are improved more reliable outcomes will be produced.