• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.17-27
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• 2012

Open Market is one of the key factors to accelerate the profit. Usually retailers sell items in several Open Market. One of the challenges for retailers is to assign categories of items with different classification systems. In this research, we propose an item category recommendation method to support appropriate products category registration. Our recommendations are based on semantic relation between existing and any other Open Market categorization. In order to analyze correlations of categories, we use Morpheme analysis, Korean Wiki Dictionary, WordNet and Google Translation API. Our proposed method recommends a category, which is most similar to a guide word by measuring semantic similarity. The experimental results show that, our system improves the system accuracy in term of search category, and retailers can easily select the appropriate categories from our proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.423-428
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• 2017

These days, various and tons of education information are rapidly increasing and spreading due to Internet and smart devices usage. Recently, as e-Learning usage increasing, many instructors and students (learners) need to set a goal to maximize learners' result of education and education system efficiency based on big data analytics via online recorded education historical data. In this paper, the author applied Word2Vec algorithm (neural network algorithm) to find similarity among education words and classification by clustering algorithm in order to objectively recognize and analyze online recorded education historical data. When the author applied the Word2Vec algorithm to education words, related-meaning words can be found, classified and get a similar vector values via learning repetition. In addition, through experimental results, the author proved the part of speech (noun, verb, adjective and adverb) have same shortest distance from the centroid by using clustering algorithm.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.215-218
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• 2005

This paper reports the current status of development of the Korean speech recognition platform (ECHOS). We implement new modules including ETSI feature extraction, backward search with trigram, and utterance verification. The ETSI feature extraction module is implemented by converting the public software to an object-oriented program. We show that trigram language modeling in the backward search pass reduces the word error rate from 23.5% to 22% on a large vocabulary continuous speech recognition task. We confirm the utterance verification module by examining word graphs with confidence score.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.37-42
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• 2012

The pseudo relevance feedback suggests that frequent words at the top documents are related to initial query. However, the main drawback associated with the term frequency method is the fact that it relies on feature independence, and disregards any dependencies that may exist between words in the text. In this paper, we propose query expansion based on word graph using term proximity. It supplements term frequency method. On TREC WT10g test collection, experimental results in MAP(Mean Average Precision) show that the proposed method achieved 6.4% improvement over language model.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.725-734
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• 2003

In this paper, we propose the text summarization method using PCA (Principal Component Analysis) and SVD (Singular Value Decomposition). The proposed method presents a summary by extracting significant sentences based on the distances between thematic words and sentences. To extract thematic words, we use both word frequency and co-occurence information that result from performing PCA. To extract significant sentences, we exploit Euclidean distances between thematic word vectors and sentence vectors that result from carrying out SVD. Experimental results using newspaper articles show that the proposed method is superior to the method using either word frequency or only PCA.

• The Korean Journal of Franchise Management
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• v.15 no.2
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• pp.39-56
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• 2024

Purpose: Delivery food continues to grow. In the past, restaurant companies directly hired delivery workers to deliver food, but now, restaurant companies use delivery service platform companies to carry out delivery work rather than directly hiring delivery workers. Therefore, this study seeks to determine the impact of delivery food benefits in the restaurant industry on brand image, trust, and word-of-mouth intention. Research design, data, and methodology: To test the hypotheses of this study, 400 questionnaires were distributed and 340 were collected. Among these, 321 questionnaires, excluding 19 questionnaires that were answered insincerely, were used in the final analysis. Result. First, delivery food benefits were found to have a significant impact on brand image and trust. Second, brand image was found to have a significant effect on trust and word-of-mouth intention. Third, trust was found to have a significant effect on word-of-mouth intention. Conclusions: First, existing research focused on studying the attributes of delivery food in the restaurant industry, but this study studied the benefits that consumers can obtain through purchase among these attributes. Second, delivery food restaurants need to design promotions and advertisements in a way that displays coupons, points, or mileage. Third, quick delivery of orders can be a competitive advantage for delivery food restaurants.

• Journal of Environmental Science International
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• v.19 no.8
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• pp.961-969
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• 2010

This study was carried out to analyze conceptional structure between visitor's satisfaction and loyalty in the national natural recreation forests. The results of this study showed that facility, natural resources and view, staff service of recreation forest had positive effect on visitor's satisfaction. Reservation system, accessibility, and usage fee of recreation forest was not related to visitor's satisfaction. In addition, visitor's satisfaction was found to have positive effect on visitor's loyalty such as revisiting intention and word of mouth. As a result, managers of recreation forest need to enhance visitor's satisfaction, to improve rate of revisiting intention and to incite word of mouth through building management strategy.

• Culinary science and hospitality research
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• v.13 no.3
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• pp.54-67
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• 2007

This study is purposed to find out the relation between the customer loyalty and customer satisfaction factors in Cheonan banquet service. To achieve the objects, customers of banquet companies in Cheonan were selected for a questionnaire survey and a total of 293 valid questionnaires are statistically analyzed, using frequency analysis, factor analysis, reliability analysis and regression analysis. The results can be summarized as follows. First, gender, occupation, family forms showed the statistically different results in the service trust. Second, service trust, menu and traffic factors have been approved to affect the revisit intention of a customer significantly. Third, service trust, menu and facilities factors have been approved to affect the word-of-mouse intention significantly. In summary, banquet managers in Cheonan have to focus on the customer satisfaction factors such as service trust, menu, traffic, facilities to meet the customers' needs.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.491-503
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• 2002

In this paper, we propose a automatic document summarization method based on Principal Component Analysis(PCA) which is one of the multivariate statistical methods. After extracting thematic words using PCA, we select the statements containing the respective extracted thematic words, and make the document summary with them. Experimental results using newspaper articles show that the proposed method is superior to the method using either word frequency or information retrieval thesaurus.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.81-84
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• 2002

멀티미디어 분야가 급성장하면서 좀더 효율적으로 멀티미디어 자료의 저장, 처리, 검색을 위한 연구가 진행되고 있다. 특히, 내용기반 시각정보 검색에 있어 지능형 시스템(Intelligent System)을 접목하여 의미적 접근을 시도하는 I-CBIR(Intelligent-Content Based Image Retrieval)에 관한 연구가 진행되고 있다. 또한, 내용기반 이미지검색 시스템에 온톨로지(Ontology)의 이론을 적용하여 이미지에 의미를 부여하여 개념적 검색이 가능하도록 노력하고 있다. 이러한 연구에서 적용된 대형의 온톨로지는 이미지 검색 시스템에 적합하지 않게 너무 방대한 정보를 가지고 있으며, 또한 시대적 변화에 대응하지 못하여 I-CBIR 시스템에서 그 효율성을 제대로 발휘하지 못하고 있다. 따라서 본 논문에서는 많은 대형 온톨로지 중에서 WordNet을 선택하여, WordNet의 구축 방법에 기반한 자동차(Car)에 대한 도메인 온톨로지(Domain Ontology)를 구축해보고, 구축된 도메인 온톨로지를 적용함으로써 더 향상된 I-CBIR 시스템이 되도록 하였다.