• Knowledge Management Research
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• v.21 no.4
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• pp.175-193
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• 2020

With the proliferation of information technology communication and smart device, the environment where contents are produced and distributed is changing. People can use the contents quickly and easily, and the content industry is attracting attention and creating newly added value by converging with other industries. Accordingly, there is a need for content-related companies to understand the quality of content perceived by users in order to succeed in content, and to use it strategically. Therefore, this study aims to examine the relationship between content quality factors, user satisfaction, and recommendation intention through empirical analysis based on an IS success model. The analysis was conducted using smartPLS3.0 based on a total of 301 survey responses. As a result of the study, it was found that content usefulness, accessible system quality, convenient system quality, service provider trust, and interaction had a significant effect on user's satisfaction. Perceived privacy protection had a significant effect on user satisfaction and recommendation intention. Lastly, it was found that user satisfaction had a significant effect on recommendation intention. The results of this study are expected to provide useful information and therefore content companies can understand about the quality perceived by users.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.437-442
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• 2021

In this study, a recommendation algorithm was implemented by learning a deep learning-based classification model for consumer data. For this purpose, a meaningful result is presented as a result of learning using ResNet50, which is commonly used in classification tasks by converting user data into images.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.101-112
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• 2011

As the society has become maturer, stress has emerged as a hot social issue. In this paper, we proposed the design and implementation of a customized contents service system for relieving users' stress. In the proposed system, we compute the stress index from a user's biometics and psychology, and recommend a combination of video, sound, aroma, and lighting based on the index and user's data such as preferences, and provide the recommended contents service. We first classify symptoms of stress and then define a tool for self assessment. We classify video, sound, aroma, and lighting contents as well according to the defined symptoms. We propose 3-phase customized recommender and service model for customized contents service. We design and implement a customized contents service system for relieving users' stress. Different from existing systems, this proposed system has an individually-customized system and provides a diverse combination of different content's types.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.346-354
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• 2009

This paper proposes a new learning recommendation system for learning patterns that educators are applying to learners using item-based method. The proposed method in this paper first collects personal learning methods based on learning information that learners are performing through the internet contents site. Then this system recommends a learning method which is estimated most properly to learners after classifying learning elements based on these information. The students of a middle school took part in the experiment in order to evaluate the proposed system, and the students were divided into three groups according to their grades. We gave inter-attribute and intra-attribute weights to learning elements applying to each group for recommending the most efficient method to improve learning achievement. The experiment showed that the learning achievement of learners in the proposed method is improved considerably compared to the previous grades.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.1-10
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• 2015

We propose and implement a personalized tour recommendation system based on ontology. We utilize user's profile, dynamic information on search in the application, web search, and facebook for personalized recommendation. We construct tour database for England based on ontology for a demo service, and recommend tour spot considering an individual preference with tour database. This dynamic and personalized tour service makes it possible for individual to plan one's own tour by considering recommended tour spots for each individual.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.10-19
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• 2010

The progress of computers and Web has given rise to a rapid increase of the quantity of the useful information, which is making the demand of recommender systems widely expanding. Like in other domains, a recommender system in a digital library is important, but there are only a few studies about the recommender system of research papers, Moreover none is there in korea to our knowledge. In the paper, we seek for a way to develop the NDSL recommender system of research papers based on the survey of related studies. We conclude that NDSL needs to modify the way to collect user's interests from explicit to implicit method, and to use user-based and memory-based collaborative filtering mixed with contents-based filtering(CF). We also suggest the method to mix two filterings and the use of personal ontology to improve user satisfaction.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.169-172
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• 2015

웹툰의 양은 방대하여 사용자가 원하는 웹툰을 찾는데 어려움이 있기 때문에 체계적인 추천 시스템이 필요하다. 하지만 기존의 추천 시스템은 조회수가 많은 인기 웹툰을 추천하는 방식과 사용자와 비슷한 연령대, 성별의 사용자들이 조회한 콘텐츠를 추천해주는 인구 통계학적 추천(demographic filtering)방식, 그리고 비슷한 사용자를 분석하여 추천해주는 협업적 추천(collaborative filtering)방식에 국한되어 있어, 개인의 성향을 반영하여 추천하고 있다고 보기 어렵다. 따라서 사용자 개인의 성향을 분석하는 방식에 대한 시도가 필요하다. 본 연구에서는 이러한 한계를 극복하기 위해서 개인의 성향을 분석하는 지표로 MBTI(Myers-Briggs Type Indicator) 유형을 이용하고, 같은 MBTI 유형의 사용자간의 협업적 필터링 추천 방식을 제안하였다. 또, 협업적 필터링 방식에서 발생하는 콜드 스타트 문제와 초기 평가자 문제를 해결하는 방안을 제시하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.195-198
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• 2015

최근 TV 서비스의 가입자 및 TV 프로그램 콘텐츠의 급격한 증가에 따라 빅데이터 처리에 적합한 추천 시스템의 필요성이 증가하고 있다. 본 논문은 사용자들의 간접 평가 데이터 기반의 추천 시스템 디자인 시, 누적된 사용자의 과거 이용내역 데이터를 저장하지 않고 새로 생성된 사용자 이용내역 데이터를 학습하는 효율적인 알고리즘이면서, 시간 흐름에 따라 사용자들의 선호도 변화 및 TV 프로그램 스케줄 변화의 추적이 가능한 토픽 모델링 기반의 알고리즘을 제안한다. 빅데이터 처리를 위해서는 분산처리 형태의 알고리즘을 피할 수 없는데, 기존의 연구들 중 토픽 모델링 기반의 추론 알고리즘의 병렬분산처리 과정 중에 핵심이 되는 부분은 많은 데이터를 여러 대의 기계에 나누어 병렬분산 학습하면서 전역변수 데이터를 동기화하는 부분이다. 그런데, 이러한 전역데이터 동기화 기술에 있어, 여러 대의 컴퓨터를 병렬분산처리하기위한 하둡 기반의 시스템 및 서버-클라이언트간의 중재, 고장 감내 시스템 등을 모두 고려한 알고리즘들이 제안되어 왔으나, 네트워크 대역폭 한계로 인해 데이터 증가에 따른 동기화 시간 지연은 피할 수 없는 부분이다. 이에, 본 논문에서는 빅데이터 처리를 위해 사용자들을 클러스터링하고, 클러스터별 제안 알고리즘으로 전역데이터 동기화를 수행한 것과 지역 데이터를 활용하여 추론 연산한 결과, 클러스터별 지역별 TV프로그램 시청 토큰 별 은닉토픽 할당 테이블을 유지할 때 추천 성능이 더욱 향상되어 나오는 결과를 확인하여, 제안된 구조의 추천 시스템 디자인의 효율성과 합리성을 확인할 수 있었다.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.329-345
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• 2021

Recommender systems use data from customers to suggest personalized products. The recommender systems can be categorized into three cases; collaborative filtering, contents-based filtering, and hybrid recommender system that combines the first two filtering methods. In this work, we introduce and compare deep learning-based recommender system using autoencoder. Autoencoder is an unsupervised deep learning that can effective solve the problem of sparsity in the data matrix. Five versions of autoencoder-based deep learning models are compared via three real data sets. The first three methods are collaborative filtering and the others are hybrid methods. The data sets are composed of customers' ratings having integer values from one to five. The three data sets are sparse data matrix with many zeroes due to non-responses.