• 패션비즈니스
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• 제23권6호
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• pp.49-63
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• 2019

The purpose of this study was to determine the satisfaction, trust and word-of-mouth intention of online information according to the type of information search by female cosmetics consumers in their 20's to 40's. For this study, online and offline surveys were conducted by 307 people. Factor, correlation, and multiple regression analysis were used to analyze the data. The main results are summarized as follows. First, the cosmetic consumer's information search types were identified as active, playful, and economic information search types. Second, the results of examinations on the effect of consumer information search types on satisfaction, reliability, and word-of-mouth intention of the online information searches showed that the active information search type had a positive effect on satisfaction, reliability, and word-of-mouth intention. The economic information search type had a positive effect on satisfaction. The active information search type was confirmed to have high satisfaction, reliability, and word-of-mouth intention for the provided information and thus, the acceptance of the provided information was high. The playful information search type was divided into continuous, habitual, and independent information search and a tendency to assign a low value to consumer information was confirmed. The economic information search type showed high satisfaction with the information obtained by searching, but also a passive attitude toward trust or word-of-mouth intention and was categorized as a passive search type. Online information search is a communication channel with a great influence that can provide various benefits to cosmetic consumers.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권8호
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• pp.1101-1112
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• 2004

본 논문에서는 웹 검색의 성능 향상을 위해 질의어 의미별 사용자 선호도를 이용한 웹 페이지의 가중치 부여 방식을 제안한다. 일반적으로 검색엔진들은 검색 질의어와 웹 페이지의 어휘 비교에 의한 관련도 측정만을 사용하여 웹 페이지의 가중치를 부여한다. 웹과 같이 방대한 자료를 대상으로 검색을 할 경우 유사한 관련도를 가진 검색 결과가 매우 많으므로 어휘 비교만으로는 중요한 웹 페이지를 선별하기 어렵다. 본 논문에서는 질의어의 의미를 구분하도록 워드넷(WordNet)을 이용한 사용자 인터페이스를 구축하고, 사용자의 클릭 수를 각 웹 페이지의 가중치에 누적함으로써 다수 사용자의 검색 행위에 의한 묵시적 평가가 웹 페이지의 검색 순위에 반영되는 검색 시스템을 구현하였다. 클릭수의 누적에 있어서 질의 어 의미별로 가중치를 구분하여 저장함으로써 일반적인 검색엔진보다 정확한 검색이 되었으며, 웹 페이지의 범주별 가중치와 질의어의 의미별 사용자 선호도를 이용함으로써 검색 시스템의 성능을 향상시킬 수 있다는 것을 20개의 어휘에 관련된 41개의 의미들을 대상으로 실험한 결과로 확인하였다.

• The Journal of the Acoustical Society of Korea
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• 제26권1E호
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• pp.21-26
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• 2007

In this paper, we propose a modified Viterbi algorithm to compensate for endpoint detection error during the decoding phase of an isolated word recognition task. Since the conventional Viterbi algorithm explores only the search space whose boundaries are fixed to the endpoints of the segmented utterance by the endpoint detector, the recognition performance is highly dependent on the accuracy level of endpoint detection. Inaccurately segmented word boundaries lead directly to recognition error. In order to relax the degradation of recognition accuracy due to endpoint detection error, we describe an unconstrained search of word boundaries and present an algorithm to explore the search space with efficiency. The proposed algorithm was evaluated by performing a variety of simulated endpoint detection error cases on an isolated word recognition task. The proposed algorithm reduced the Word Error Rate (WER) considerably, from 84.4% to 10.6%, while consuming only a little more computation power.

• 대한음성학회지:말소리
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• 제59호
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• pp.53-68
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• 2006

We report the evaluation results of the Korean speech recognition platform called ECHOS. The platform has an object-oriented and reusable architecture so that researchers can easily evaluate their own algorithms. The platform has all intrinsic modules to build a large vocabulary speech recognizer: Noise reduction, end-point detection, feature extraction, hidden Markov model (HMM)-based acoustic modeling, cross-word modeling, n-gram language modeling, n-best search, word graph generation, and Korean-specific language processing. The platform supports both lexical search trees and finite-state networks. It performs word-dependent n-best search with bigram in the forward search stage, and rescores the lattice with trigram in the backward stage. In an 8000-word continuous speech recognition task, the platform with a lexical tree increases 40% of word errors but decreases 50% of recognition time compared to the HTK platform with flat lexicon. ECHOS reduces 40% of recognition errors through incorporation of cross-word modeling. With the number of Gaussian mixtures increasing to 16, it yields word accuracy comparable to the previous lexical tree-based platform, Julius.

• 한국식품조리과학회지
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• 제22권2호
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• pp.213-221
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• 2006

Although motivation and information search have both been studied continuously and separately as important marketing strategies, the relation between cause and effect has received little research attention. Therefore the objective of this study was to research the causal relationships between motivation and information search method. Frequency analysis and reliability analysis, factor analysis, and SEM(Structure Equation Model) were adopted to analyze the data. Motivation was divided into 5 factors which significantly influenced information search method. Factor 1, 'Reception and congratulation', influenced information search positively through 'newspaper, magazine', and 'word of mouth' but negatively through 'TV-advertising' and 'Flyer, Press copy'. Factor 2, 'Change of dietary life', influenced positively 'TV-advertising'. Factor 3, 'Economic saving', influenced positively 'newspaper, magazine', and 'the e-mail's advertising' Factor 4, 'Preference motivation', influenced negatively 'word of mouth' Factor 5, 'Advertisement and companion's need', influenced positively 'newspaper, magazine', and 'the e-mail's advertising' but negatively 'TV-advertising' As a result, customers appeared to choose various information search methods according to their eating-out motivation. 'The e-mail's advertising', and 'word of mouth' are popular among customers' information search methods. Therefore, food-service corporations need to try eliminating negative images of various advertisements and activate positive word of mouth marketing, promotion through internet.

• 한국콘텐츠학회논문지
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• 제16권10호
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• pp.687-692
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• 2016

기존의 문서 검색 방법론은 TF-IDF와 같은 벡터공간모델을 활용한 키워드 기반 방법론을 사용한다. 키워드 기반의 문서검색방법론으로는 문제가 몇몇 문제점이 나타날 수 있다. 먼저 몇 개의 키워드로 전체의 의미를 나타내기 힘들 수 있다. 또 기존의 키워드 기반의 방법론을 사용하면 의미상으로 비슷하지만 모양이 다른 동의어를 사용한 문서의 경우 두 문서 간에 일치하는 단어들의 특성치만 고려하여 관련이 있는 문서를 제대로 검색하지 못하거나 그 유사도를 낮게 평가할 수 있다. 본 연구는 문서를 기반으로 한 검색방법을 제안한다. Centrality를 사용해 쿼리 문서의 특성 벡터를 구하고 Word2vec알고리즘을 사용하여 단어의 모양이 아닌 단어의 의미를 고려할 수 있는 특성 벡터를 만들어 검색 성능의 향상과 더불어 유사한 단어를 사용한 문서를 찾을 수 있다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권4호
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• pp.299-303
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• 2009

특허 정보 검색은 연구 및 기술 개발에 앞서 선행연구의 존재 여부를 확인하기 위한 사전 조사 목적으로 주로 사용된다. 이러한 특히 정보 검색에서 원하는 정보를 얻지 못하는 원인은 다양하다. 그 중에서 본 연구는 키워드 불일치에 의한 정보 누락을 최소화하기 위한 대체어 후보 추출 방법을 제안한다. 본 연구에서 제안하는 대체어 후보 추출 방법은 문장 내에서 함께 쓰이는 단어들이 비슷한 두 단어는 서로 비슷한 의미를 지닐 것이다라는 직관적 가설을 전제로 한다. 이와 같은 가설을 만족하는 대체어를 추출하기 위해서 본 연구에서는 분류별 집중도, 신뢰도를 이용한 연관단어뭉치, 연관단어 뭉치간 코사인 유사도 및 순위 보정 기법을 제안한다. 본 연구에서 제안한 대체어 후보 추출 방법의 성능은 대체어 유형별로 작성된 평가지표를 이용하여 재현율을 측정함으로써 평가하였으며, 제안 방법이 문서 벡터공간 모델의 성능보다 더 우수한 것으로 나타났다.

• 지식경영연구
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• 제15권3호
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• pp.141-168
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• 2014

Purchasing through Internet shopping mall has more uncertainty compared with offline shopping mall. Previous studies have presented that trust plays a role of reducing uncertainty and increasing purchasing intention. In this study, we suggest that third-party assurance and word-of-mouth contribute to the formation of trust. In addition, we also propose that ease of product evaluation plays moderating roles in the relationships between third-party assurance, word-of-mouth and trust. For this study, we collected sample data from two groups consisting of online shoppers purchasing the search goods and experience goods categorized by type of ease of product evaluation. Empirical results show that word-of-mouth and third-party assurance have different effects on trust in two groups. The third-party assurance has a stronger impact on trust in online shopping group of the search goods than in the experience goods, while word-of-mouth in the online community has a stronger impact on trust in online shopping group of the experience goods than in the search goods. We expect that this result will provide researchers and managers who are interested in trust formation factors in online shopping mall with useful theoretical and practical implications.

• Journal of Information Technology Applications and Management
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• 제10권2호
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• pp.33-41
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• 2003

It is not easy to find desired information in the world wide web. In this research, we introduce a notion of concept network that is useful in finding information if it is used in search engines that are specialized in domains such as medicine, law or engineering. The concept network that we propose is a network in which nodes represent significant concepts in the domain, and links represent relationships between the concepts. We may use the concept network constructor as a preprocessor to speci-alized search engines. When user enters a target word to find information, our system generates and displays a concept network in which nodes are con-cepts that are closely related with the target word. By reviewing the network, user may confirm that the target word is properly selected for his intention, otherwise he may replace the target word with better ones discovered in the network. In this research, we propose a detailed method to construct concept net-work, implemented a prototypical system that constructs concept networks, and illustrate its usefulness by demonstrating a practical case.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.960-968
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• 2018

The mobile visual search service uses a query image to acquire linkage information through pre-constructed DB search. From the standpoint of this purpose, it would be more useful if you could perform a search on a web-based keyword search system instead of a pre-built DB search. In this paper, we propose a representative query extraction algorithm to be used as a keyword on a web-based search system. To do this, we use image classification labels generated by the CNN (Convolutional Neural Network) algorithm based on Deep Learning, which has a remarkable performance in image recognition. In the query extraction algorithm, dictionary meaningful words are extracted using Wikipedia, and hierarchical categories are constructed using WordNet. The performance of the proposed algorithm is evaluated by measuring the system response time.