• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.81-93
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• 2021

This study examined the effects of cosmetic selection attributes on consumers' purchase propensity and recommend intention. In addition, the moderating effect according to the consumer's deal proneness was verified. Finally, a multi-group analysis was conducted to verify the difference in the research model path according to the information source. Through this study, the selection attributes of consumers who purchase cosmetics were clarified. This study aims to meet the needs and demands of the related industry for more detailed and effective strategic insights by clarifying the structure of the influence of these selection attributes on purchase intention and recommendation intention according to the discount purchase intention. In order to achieve the research objectives due to this necessity, a questionnaire of 258 Korean female consumers was collected and used for research. The analysis results showed that product selection attributes, purchase intention, and recommendation intention all had a positive influence. As a result of analyzing the moderation effect according to the consumer's Deal Proneness, the results showed a moderating effect between the selection attribute and the purchase propensity, the selection attribute and the recommend intention, and the purchase propensity and the recommend intention. Finally, it was partially adopted as a result of conducting a multi-group analysis to verify whether individual paths of the model differ according to information sources.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.13-21
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• 2019

The tendency of buyers of web information is changing from the cost-effectiveness which emphasizes the performance over the price to the cost-satisfaction which emphasizes the psychological satisfaction of the buyer. In music recommendation, one of the methods to increase psychological satisfaction is to use the music mood. In this paper, a music recommendation method considering the mood tag and the synonyms tag is proposed and, as an intermediate result of the proposed method, mood tags and music pieces are expressed in Thayer's AV space and then their distribution are analyzed. The analysis result shows the distributions of mood tags and the ones of music pieces are similar, which implies that the proposed recommendation method can provide significant results. In the future, the music recommendation performance will be analyzed.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.759-760
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• 2020

최근 사용자, 상품, 그리고 상품의 메타 정보 사이의 관계를 표현한 지식 그래프 (knowledge graph) 가 추천 시스템 분야에서 많은 관심을 받고 있으며 활발히 이용되고 있다. 하지만 기존의 지식 그래프는 각 노드 (사용자, 상품, 메타 정보 등) 사이의 단순한 사실 관계만을 표현하고 있으며, 이는 사용자의 선호도를 정확히 파악하는 데 한계가 있다. 본 논문에서는 지식 그래프의 정보 부족 문제를 보완하기 위해 각 상품에 남겨진 텍스트 리뷰를 감정 분석 (sentiment analysis) 하고, 이를 각 노드 간의 선호도 정보로 활용하여 지식 그래프를 구축하는 방법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.401-404
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• 2020

현재 전자상거래에서 사용되는 협업 필터링은 고객이 입력한 평점 정보를 이용하여 추천 시스템을 구축한다. 하지만 기존의 평점 정보는 고객이 직접 입력해야 하므로 데이터 희소생의 문제가 있고 허위정보를 가려내지 못한다는 문제점 또한 존재한다. 본 논문에서는 기존 평점 정보 기반의 협업 필터링 추천 시스템의 문제점을 해결하기 위해, 온라인 고객 행동 정보를 활용한 협업 필터링 알고리즘을 제안하였다. 실험 결과 본 연구에서 제안한 Collaborative Filtering based on Online Behavior Information (BICF) 알고리즘이 기존의 평점 기반 협업 필터링 방식보다 우수한 성능을 보임을 보여주었다.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.1-22
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• 2016

News contents has been collected, selected, edited and sometimes distorted by the news recommendation mechanisms of online portals in nowadays. Prior studies had not confirmed the consensus of newsworthiness, and they had not tried to empirically validate the impacts of newsworthiness on audience reactions. This study challenged to summarize the concepts of newsworthiness and validate the impact of representativeness of both editor's and audience's perspective on audience reactions as perceived news quality, trust on news portal, perceived usefulness, service satisfaction, loyalty, continuous usage intention, and word-of-mouth intention by adopting the representativeness heuristics method and information adoption model. 357 valid data had been collected using a scenario survey method. Subjects in each groups are exposed by 3 news recommendation mechanisms: 1) the time-priority news exposure mechanism (control group), 2) the reference-score-based news recommendation mechanism (a single treatment group), and 3) the major-news-priority exposure mechanism sorting by the reference scores made by peer audiences (the mixed treatment group). Data had been analyzed by the MANOVA and PLS method. MANOVA results indicate that only mixed method of both editor and audience recommendation mechanisms impacts on perceived news quality and trust. PLS results indicate that perceived news quality and trust could significantly affect on the perceived usefulness, service satisfaction, loyalty, continuance usage, and word-of-mouth intention. This study would contributions to empathize the role of information technology in media industry, to conceptualize the news value in the balanced views of both editors and audiences, and to empirically validate the benefits of news recommendation mechanisms in academy. For practice, the results of this study suggest that online news portals would be better to make mixed news recommendation mechanisms to attract audiences.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.1
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• pp.64-70
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• 2001

To verify the newly developed decision assisting expert system for nitrogen fertilizer application NES to Illinois cornfields, a couple of N rate studies from Dr. Howard and five Illinois Agricultural Experiment Stations were applied. Four types of recommendations including the current Illinois recommendation, Hoeft recommendation, NES, and maximum economic recommendation were compared with each other for the crop yields, profits, recovery rate, and N losses to cornfields. The N rate of NES recommendation, considering productivity index (PI), soil organic matter content (SOM), and pre-sidedressing nitrate concentration (PSNT) level, was the lowest in comparison to those of other recommendations. However, N recovery rate in NES was generally higher and the resulting N loss was lower than others. But, adherence to the recommendations may also reduce farmers income if environmental expense did not considered. Therefore, NES will be more effective by adding the factors including environmental expense, tillage systems, crop rotation, and other agricultural management parameters.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2001.11a
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• pp.89-90
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• 2001

최근의 대기확산 모델링 분야는 모델링 수행에 큰 장애요인이었던 계산속도에 의한 제한요소가 Computer H/W 성능의 향상으로 상당히 제거되면서, 학계의 연구를 통해서 보다 진보된 확산이론을 사용하는 새로운 개념의 모델들이 속속 개발되고 있다. 이 중, 일부 모델들은 2000년도부터 미국 EPA(환경보호청)으로부터 새롭게 추천받고 있는데, 근래 미국 EPA에서 새로이 추천하고 있는 모델로서 ISC3-PRIME, AERMOD, 및 CALPUFF이 있다. (중략)

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.153-171
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• 2017

Many readers tend to read books of a specific author and to expand their reading areas according to the author. This study chose Edgar Allan Poe and analyzed the image of the author using co-recommended authors and books by other readers. The frequencies of co-occurred authors and books were investigated and the relations of authors and books were analyzed with network analysis methods. As a result, genre images of Poe, related authors, and related books are discovered. This study also suggested the methods to identify the image of a author, related author groups, and related books for libraries' reading programs and book curation.

• The Journal of Society for e-Business Studies
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• v.14 no.1
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• pp.1-11
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• 2009

Many neighbors are needed for making the recommendation quality better and stable in collaborative filtering. Furthermore, the quality is not so good mainly due to a reason that high similarity between two users does not guarantee the same preference to items considered for recommendation. Dissimilar users who have consistency in item selection can be useful for predicting preferences. This paper proposes a new collaborative filtering method, defining trust based on consistency for improving this phenomenon. Empirical studies show that such a method reduces the number of neighbors required to make the recommendation quality stable and the recommendation quality itself is also significantly improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.103-108
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• 2021

It is difficult to predict an increase or decrease of stock price because of uncertainty. Researches for prediction of stock price using AI technology have been done for a long time. Recently stock buy/sell recommend programs called by Robot Advisor using AI machine learning technology are used. In this paper, to develop a stock buy/sell recommend system using AI technology, an core engine of this system is designed. An analysis and design method of a stock buy/sell recommend system software using AI machine learning technology will be presented by showing user requirement analysis using object-oriented analysis method, flowchart and screen design.