• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.383-384
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• 2018

최근 주말의 개념이 확장되고 일상보다 여유를 우위를 두는 사람들이 많아짐에 따라 여행 산업이 발전하고 있다. 본 논문에서는 사용자 성향을 기반으로 하이브리드 협업 필터링을 이용한 여행지 추천 기법을 제안한다. 사용자별 여행지 선호도를 생성하고 사용자 기반 협업 필터링을 통해 후보 여행지를 생성하고 아이템 기반 협업 필터링을 수행하여 여행지 성향 점수를 생성한다. 여행지 성향 점수와 여행지별 성향을 고려하여 최종 여행지를 추천한다.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.55-66
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• 2010

Propensity score adjustment(PSA) has been suggested as approach to adjustment for volunteer internet survey. PSA attempts to decrease the biases arising from noncoverage and nonprobability sampling in volunteer panel internet surveys. Although PSA is an appealing method, its application for internet survey regarding Korea presidential election and its effectiveness is not well investigated. In this study, we compare the Ni Korea internet survey with the telephone survey conducted by MBMR and KBS for 2007 Korean presidential election. The result of study show that the accuracy of internet survey can be improved by using PSA. And it is critical to include covariates that highly related to the voting tendency and the role of nondemographic variables seems important to improving PSA for Korea presidential election prediction.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.19-20
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• 2018

전자상거래가 대중화되면서 다양한 아이템을 손쉽게 구매할 수 있는 환경이 조성되었다. 전자상거래에서 소비자의 구매율을 향상시키기 위해 개인 맞춤 추천 서비스가 요구되고 있다. 본 논문에서는 사용자 성향과 제품의 신뢰성을 고려한 상품 추천 기법을 제안한다. 사용자의 성향은 찜하기, 리뷰, 클릭 등과 같은 다양한 사용자의 행위 분석을 통해 추출하고 상품의 신뢰성은 SNS에서의 언급 수와 서비스내의 사용자 행위를 통해 계산한다. 계산된 성향을 기반으로 협업 필터링을 수행하여 상품별 예측 점수를 생성하고 상품의 신뢰성을 고려하여 최종적인 추천 목록을 생성한다.

• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.767-787
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• 2018

Technology-driven innovation and job-creation has each been the subject of much scholarly attention, but have largely been considered separately rather than in conjunction with each other. While the previous literature on economics pinpointed the macro effects on industry-level, this study explores the micro-level comparisons on innovation sources over the employment and financial performances. The PSM (propensity-score matching) analysis presents that firms, involved in an inward technology, tend to have higher employees with dominant technology capabilities than in-house R&D firms. The in-house R&D firms, on the contrary, have superior financial performances, suggesting that external technology commercialized firms suffer from low transformative efficiency. The mediation test analysis corroborates that the external technology-driven innovation induces more human resources in internalizing the exogenous technology. The positive relationship between R&D innovation and employment allow verification of the government's intervention in the promotion of technology commercialization in public sector. On the other hand, it also signals that the policy needs to enhance the recipient firms' commercializing capacity rather than a 'one-hit' transaction.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.189-196
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• 2011

In this study, we perform a simulation study to compare frequently used standardization methods for interview scores based on trimmed mean, rank mean, and z-score mean. In this simulation study we assume that interviewer's score is influenced by a weighted average of true interviewee's true score and independent noise whose weight is determined by the professionality of the interviewer. In other words, as interviewer's professionality increases, the observed score becomes closer to the true score and if interviewer's professionality decreases, the observed score becomes closer to the noise instead of the true score. By adding interviewer's tendency bias to the weighed average, final interviewee's score is assumed to be observed. In this simulation, the interviewers's cores for each method are computed and then the method is considered best whose rank correlation between the method's scores and the true scores is highest. Simulation results show that when the true score is from normal distributions, z-score mean is best in general and when the true score is from Laplace distributions, z-score mean is better than rank mean in full interview system, where all interviewers meet all interviewees, and rank mean is better than z-score mean in half split interview system, where the interviewers meet only half of the interviewees. Trimmed mean is worst in general.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.51-59
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• 2010

There have been proposed several movie recommendation algorithms based on Collaborative Filtering(CF). CF decides neighbors whose ratings are the most similar to each other and it predicts how well users will like new movies, based on ratings from neighbors. This paper proposes a new method to improve the result predicted by CF based on genres of the movies seen by users. The proposed method can be combined to the most of all existing CF algorithms. In this paper, a performance evaluation has been conducted between an existing simple CF algorithm and CF-Genre that is the proposed genre-based method added to the CF algorithm. The result shows that CF-Genre improves 3.3% in prediction performance over existing CF algorithms.

• Proceedings of the Korean Association for Survey Research Conference
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• 2005.12a
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• pp.169-183
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• 2005

This paper considers formation of nonresponse weighting adjustment cell for handling unit nonresponse in sample surveys. We propose a multivariate regression tree mehtod for segmentation using the variable of interest and the estimated response probability simultaneously to construct effective nonresponse adjustment cell. One is using only response data and the other is using response and nonresponse data. These two cases are compared in terms of bias.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.242-247
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• 2009

Recently an intelligent system is developed for the service what users want not a passive system which just answered user's request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and a fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the data that is user's rating about movies. We predicted the user's rating using this data. The accuracy of this prediction system is determined by computing the RMSE(root mean square error) of the system's prediction against the actual rating about the each movie and is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.171-182
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• 2019

As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.

• Health Policy and Management
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• v.34 no.1
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• pp.87-93
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• 2024

Background: In recent years, studies have shown conflicting results regarding the benefits of dietary supplements in reducing healthcare expenditures. This study aimed to address this inconsistency by examining the association between supplement consumption and health expenditures using nationally representative data from the Korea Health Panel Survey (2019-2020). Methods: A 1:1 matched case-control dataset was established using propensity score matching technique based on supplement consumption. Then, total annual healthcare expenditures were compared between the two groups. In addition, a multivariate regression analysis (Proc Surveyreg) was performed to determine the association between the supplement consumption and medical costs. Results: The supplement user group spent about 1.72 million Korean won, while the non-user group spent about 1.43 million Korean won on medical services (p=0.0186). The results of multivariate regression showed that the costs were approximately 26.15% higher in the user group than in the non-user group (p=0.0004). Conclusion: Contrary to the previous studies that have shown the benefits of supplement use in reducing healthcare costs, this study showed that those who consistently consumed supplements spent more on medical services. This can be interpreted in the same context as previous studies suggesting that dietary supplement intake is a healthy behavior for managing one's health. However, we caution against drawing firm conclusions due to data limitations. Further analysis using patient-level epidemiologic data is needed.