• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.59-67
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• 2013

In this paper, we propose the LAR_CF, latent attribute rating-based collaborative filtering, that is robust to data sparsity problem which is one of traditional problems caused of decreasing rating prediction accuracy. As compared with that existing collaborative filtering method uses a preference rating rated by users as feature vector to calculate similarity between objects, the proposed method improves data sparsity problem using unique attributes of two target objects with existing explicit preference. We consider MovieLens 100k dataset and its item attributes to evaluate the LAR_CF. As a result of artificial data sparsity and full-rating experiments, we confirmed that rating prediction accuracy can be improved rating prediction accuracy in data sparsity condition by the LAR_CF.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.99-107
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• 2010

Analyzing mode choice among transportation demand estimate procedures is complicated and understanding characteristics of travelers is also difficult. Generally, it is well known that traveler choose mode considering psychometric factors and characteristic besides socio-demographic indicators. Accordingly, many researches has investigated on methodology that can be applied in mode choice to reflect psychometric factor or specific preference. Latent Class Analysis among various studies is recognized as the theoretically potential approach. This study focuses on class segmented using latent class cluster to analyze impact that included psychometric factors and characteristics on mode choice. It also provides evidence that mode choice model for each class and mode choice model not considering latent class are different. This study based on citizen's stated preference and revealed preference on a new transit on the Han river shows that latent class cluster analysis is the potential approach considering latent preference.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.3
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• pp.337-350
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• 2019

The Gwangyang City of Jeonnam Province, is one of the steel cities representing the Korea. Gwangyang Steelworks are the core of the local economy, and 59 firms of POSCO Outsourcing Partner Cooperation(POSPA) have been employed 9,300 to 9,500 peoples, and have been acted as an central incubator for job creation. POSPA, however, are suffering from the retirement of company in young age group, in their 20s and 30s. The purpose of this study is to analyze potential job seekers' perceptions and job preferences for POSPA are suffering from job openings. In order to this research purpose, it used questionary survey, and sample groups were divided into three areas, the Eastern Jeonnam Province, Gwangju, and the Seoul metropolitan area. Potential job seekers' perceptions for POSPA was low, and perceptions on firm and job opportunity information was lower. This characteristics were the same as those of respondents living in eastern South Jeolla Province. Potential job seekers, however, showed high preference for finding job at POSPA. A place-based policy considering the local labor market is needed to resolve the mismatch between the difficulty of finding a labor and the difficulty of finding a job.

• Journal of KIISE
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• v.44 no.6
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• pp.621-627
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• 2017

Collaborative filtering is a technique that effectively recommends unrated items for users. Collaborative filtering is based on the similarity of the items evaluated by users. The existing top-N recommendation methods are based on pair-wise and list-wise preference models. However, these methods do not effectively represent the relative preference of items that are evaluated by users, and can not reflect the importance of each item. In this paper, we propose a new method to represent user's latent preference by combining an existing preference model and the notion of inverse user frequency. The proposed method improves the accuracy of existing methods by up to two times.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.135-136
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• 2016

최근 각종 사물인터넷 제품이 시장에 출시되면서 관련 업체들은 시장 선점을 위해 표준화 플랫폼 분야에서 경쟁을 벌이고 있다. 또한 이동통신 3사를 중심으로 가정용 사물인터넷 서비스를 앞 다투어 출시하면서 시장을 뜨겁게 달구고 있다. 대표적 출시된 서비스는 스마트홈 관련 서비스가 있다. 이동통신 기반 사물인터넷 서비스는 초기 단계에 머물고 있으며 지속적으로 다양한 서비스가 출시될 것으로 예상된다. 이러한 시점에 이미 출시된 서비스를 중심으로 사물인터넷 서비스에 대한 선호도 분석을 기획하게 되었다. 새로운 제품이나 서비스가 출시되는 경우에 잠재적인 구매자들이 제품 및 서비스가 어떠한 속성에 영향을 받아 그 제품이나 서비스를 선택하게 되는지에 대한 연구는 연구자에게 매우 흥미로운 주제이다. 따라서 문헌분석과 시장조사를 통해 사물인터넷 서비스를 고찰하여 선호도 분석 개념모델을 완성하고자 한다. 또한 이동통신 3사에 근무하는 직원(전문가)를 중심으로 설문을 통해 선호도 분석을 하고자 한다. 분석결과를 바탕으로 연구의 한계와 이론적 실무적 시사점을 제시하고자 한다.

• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.581-586
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• 2007

정부의 IT839정책과 함께 유비쿼터스 환경이 실현되고 있는 요즘, 홈 네트워크 산업은 크게 성장하고 있는 추세이다. 이에 발맞추어 홈네트워크 산업의 활성화를 위해 정책 제안이나 현황 보고와 같은 연구들이 많이 이루어졌다. 그러나 이용자들의 입장에서 홈네트워크 서비스에 대한 관심이나 선호분포에 관한 연구는 부족하다. 따라서 븐 연구의 목적은 홈네트워크 서비스들에 대한 이용자들의 선호도를 조사하는데 있다. 이를 위해 먼저 다양한 홈네트워크 서비스들을 특성에 따라 분류한 후 잠재 이용자들을 대상으로 각 서비스에 대한 호감도를 조사하였다. 그 후 잠재 이용자들의 특성에 따라 홈네트워크 서비스들과 대응일치분석(Correspondence Analysis)을 하였다. 연구 결과 모든 잠재 이용자들이 안전관련 서비스에 높은 관심을 표하였으며 특히 고령일수록 그 관심은 더 높게 나타났다. 그리고 인터넷을 거의 사용하지 않는 집단은 홈네트워크 서비스에 낮은 관심을 보였다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.233-244
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• 2009

This study defines the silver portal as a specialized internet portal that will be probably popular with the pre-senior/senior internet users who are over 40, sooner or later. The purpose of the study is to analyze what types of online contents on the silver portal are mostly preferred by the pre-senior/senior, and does the preference of those content types vary according to the demographic and the internet usage characteristics of the pre-senior. The study also is to suggest some research propositions, based on the analysis results, that could be applied to empirical studies in the future which deal with various research questions on the silver portal. To accomplish these research purposes, the study performed a survey for the prospective silver portal users who are living in the areas of Seoul and Kyeonggi, South Korea, and did various statistical analyses. This study found that the preference of content types of the silver portal vary according to some personal characteristics of the prospective silver portal users, and moreover proposed a few of research propositions on the relationships between personal characteristics and preferred content types.

• Journal of Korean Society of Transportation
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• v.28 no.4
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• pp.119-128
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• 2010

Mode choice behavior is associated with travelers' latent behavior that is an unobservable preference to travel behavior or mode characteristics. This paper specifically addresses the problem of unobservable factors, that is latent behavior, in mode choice models. Consideration of latent behavior in mode choice models reduces the errors that come from unobservable factors. In this study, the authors defined the latent variables that mean a quantitative latent behavior factors, and developed the combined RP/SP model with latent variables using the mode choice behavior survey data. The data has traveler's revealed preference of existent modes along the Han River and stated preference of new water transit on the Han River. Also, The data has travelers' latent behavior. Latent variables were defined by factor analysis using the latent behaviour data. In conclusion, it is significant that the relationship between traveler's latent behavior and mode choice behavior. In addition, the goodness-of-fit of the mode choice models with latent variables are better than the model without latent variables.

• Korean Journal of Environment and Ecology
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• v.30 no.3
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• pp.434-442
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• 2016

This study examined differences in the preference of the fest therapy program regarding stress level. Using convenience sampling method, the surveys on the preferred type of forest healing program and social and psychological stress scales was carried out for adult male and female. As a basis of Psycho social Stress Scale (PWI-SF: Psychosocial Well-being Index Short Form), the adult 620 people were classified such as healthy group, potential stress group, high-risk stress group. The data were analyzed by use of SPSS 21.0 program. To see the difference in preferences for forest therapy program between the three groups according to stress levels, it was analyzed using one-way ANOVA. Depending on the stress levels, there were differences in the preferences of forest healing program such as breathing, breathing exercises, walking in the forest, listening to the sound of water flowing, viewing the forest, counseling, consultation and expert coaching, stress-related lectures, communication-related lectures, forest bathing wind bathing sun bathing. High-risk stress group preferred cognitive based program such as counseling, consultation and expert coaching, stress-related lectures, communication-related lectures. Healthy group appeared to prefer highly emotional approach of the program to take advantage of the five senses such as breathing, breathing exercises, walking in the forest, listening to the sound of water flowing, viewing the forest, forest bathing, wind bathing, sun bathing. Noticeable preference difference was not observed in the potential stress group. It is hoped this study will serve as a basis for the development of forest healing program regarding stress level.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.