• CRM연구
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• v.4 no.1
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• pp.55-68
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• 2011

Recently with the development of Web services by utilizing a variety of web content, the studies on user experience and personalization based on web usage has attracted much attention. Majority of personalized analysis are have been carried out based on existing data, primarily using the database and statistical models. These approaches are difficult to reflect in a timely mannerm, and are limited to reflect the true behavioral characteristics because the data itself was just a result of customers' behaviors. However, recent studies and commercial products on web analytics try to track and analyze all of the actions from landing to exit to provide personalized service. In this study, by analyzing the customer's click-stream behaviors, we define U-Score(Usage Score), P-Score (Preference Score), M-Score(Mania Score) to indicate variety of customer preferences. With the devised three indicators, we can identify the customer's preferences more precisely, provide in-depth customer reports and customer relationship management, and utilize personalized recommender services.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1565-1571
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• 2016

This paper is an empirical study of chinese consumers' lifestyle by country of origin effect of mobile phone. The data we analyze in this paper was collected and studied in Kim et al. (2006, 2007). We classify the respondents into the four groups according to the responses of country of origin effect of mobile phone. The four groups are group K (preference in made in Korea), group J (preference in made in Japan), group U (preference in made in USA), and group C (preference in made in China). One-way ANOVA and stepwise discriminant analysis are applied to classify the training sample which consists of 89 lifestyle variables and two personnel information. It is observed that group K is more open-minded, out front, aggressive, and self-assertive compared to group C.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.716-720
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• 2006

Fuzzy sets are good for abstracting and unifying information using natural language like terms. However, fuzzy sets embody vagueness and users may have different attitude to the vagueness, each user may choose difference one as the best among several fuzzy values. In this paper, we develop a method teaming a user's, preference on fuzzy values and select one which fits to his preference. Users' preferences are modeled with artificial neural networks. We gather learning data from users by asking to choose the best from two fuzzy values in several representative cases of comparing two fuzzy sets. In order to establish tile representative comparing cases, we enumerate more than 600 cases and cluster them into several groups. Neural networks ate trained with the users' answer and the given two fuzzy values in each case. Experiments show that the proposed method produces outputs closet to users' preference than other methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.451-454
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• 2016

최근 스마트폰의 보급률이 높아짐에 따라, 사용자들이 스마트폰을 사용하여 컨텐츠를 즐기는 시간이 많아졌다. 이후 모바일 웹에서 서비스되는 만화들이 연달아 대중들의 이목을 끌게 되어 네이버 웹툰, 다음 웹툰 등 웹툰 서비스 및 웹툰 플랫폼이 증가하고 있다. 또한 웹툰 데이터의 가치와 신뢰성도 점점 높아지고 있어, 영화 애니메이션 게임 등 콘텐츠 사업에 많은 데이터가 사용되고 있다. 따라서 본 논문에서는 나이, 성별, 선호 카테고리, 선호 웹툰 플랫폼 등과 같은 개인 성향 기반으로 협업 필터링 방법을 적용하고, 웹툰의 방대한 데이터를 효과적으로 관리하기 위해 클라우드 컴퓨팅 시스템인 AWS(Amazon Web Service)를 이용하여 개인 성향에 맞게 웹툰을 추천해주는 웹툰 추천 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.241-244
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• 2008

내비게이션 시장의 확대에 따라 주요 서비스인 길안내 외에 다양한 콘텐츠 제공 기술개발로 경쟁력을 갖춰 나갈 필요성이 대두되었다. 이러한 흐름에 발맞추어 운전자의 특성, 관심사를 고려, 운전자가 선호할 만한 서비스 정보를 제공하여 내비게이션의 경쟁력을 갖출 수 있는 방법을 제안한다. 본 논문에서는 데이터 마이닝, 필터링과 추천방법을 통하여 기존의 내비게이션이 경로를 탐색할 때 운전자의 기호와는 상관없는 정보를 제공한 것과 다르게 운전자가 선호할 만한 서비스 정보를 효율적으로 도출하는 방법을 제안 한다. 또한 내비게이션이 제공하는 불필요한 정보를 제함으로써 빠르고 효율적인 데이터관리를 할 수 있도록 한다.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.329-330
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• 2016

정부의 공공데이터 활성화 전략에 의해 행정기관과 공공기관의 정보공개 및 재이용이 활발해 지고 있다. 이러한 분위기와 함께 민간에서의 공공데이터 활용이 점점 늘어나고 있다. 하지만 당초 기대치에 못 미치는 성과와 공공데이터 개방하는 기관에는 정보화시스템을 운영하는 비용은 증가하였다. 따라서 상대적으로 민간에서 선호하는 문화관광 공공데이터를 통해 공공데이터의 효율성을 분석한 후 시사점을 도출한다.

• Journal of Advanced Navigation Technology
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• v.18 no.5
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• pp.468-475
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• 2014

The recommend system is getting more difficult real time recommend by lager preference data set, computing power and recommend algorithm. For this reason, recommend system is proceeding actively one's studies toward distribute processing method of large preference data set. This paper studied distribute processing method of large preference data set using hadoop distribute processing platform and mahout machine learning library. The recommend algorithm is used Co-occurrence Matrix similar to item Collaborative Filtering. The Co-occurrence Matrix can do distribute processing by many node of hadoop cluster, and it needs many computation scale but can reduce computation scale by distribute processing. This paper has simplified distribute processing of co-occurrence matrix by changes over from four stage to three stage. As a result, this paper can reduce mapreduce job and can generate recommend file. And it has a fast processing speed, and reduce map output data.

• Journal of Korean Library and Information Science Society
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• v.35 no.4
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• pp.229-250
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• 2004

The purpose of this study is to investigate the correlation between users' preferences of Personality types and their attitude towards patterns of Web OPAC use mainly focus on their search behaviour and their preferences for the interface. Data res collected through the MBTI test and self-designed online questionnaire. The original MBTI personality types were re-coded into 4 categories of preferences of personality types, that is, E(Extraversion), I(Introversion), S(Sensing), N(iNtuition), T(Thinking)-F(Feeling), and J(Judging)-P(Perception) and then analysed their correlation with patterns of Web OPAC use by Person's correlation coefficient (r) in SPSS Windows Ver. 11.0. It is noteworthy that 9 out of 28 factors of Web OPAC search behaviour and preferences for interfaces show statistically significant correlation with users' MBTI preferences of personality types.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.443-449
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• 2014

Preferences of users on movies are observables of various factors that are related with user attributes and movie features. For movie recommendation, analysis methods for relation among users, movies, and preference patterns are mandatory. As a relational analysis tool, we focus on the Infinite Relational Model (IRM) which was introduced as a tool for multiple concept search. We show that IRM-based co-clustering on preference patterns and movie descriptors can be used as the first tool for movie recommender methods, especially content-based filtering approaches. By introducing a set of well-defined tag sets for movies and doing three-way co-clustering on a movie-rating matrix and a movie-tag matrix, we discovered various explainable relations among users and movies. We suggest various usages of IRM-based co-clustering, espcially, for incremental and dynamic recommender systems.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.323-330
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• 2021

In this paper, a study was conducted to understand how students' attitudes and tendencies toward reading affect the newly emerging coding education. Relevant data were collected by dividing it into three areas (reading, coding, and leisure). In the reading area, data on preference books, preferred types of reading and etc were collected. In the coding area, prior learning of coding, main tasks using a computer, time used for learning and etc were collected. In the leisure area, main leisure activities and hours of spent leisure time per one week were collected. Using the collected data, we classified and analyzed the data based on the preferred reading method to identify the problems of non-major students who have difficulties in coding education. In coding education, the excerpts reading student group showed the best achievement (average 60.1), and the extensive reading group showed the lowest achievement (average 48.4). The students who read extensively spent more time in coding study than the group of students who preferred other reading methods, but showed the lowest achievement.