• Journal of Fashion Business
• /
• v.23 no.6
• /
• pp.49-63
• /
• 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.

• Journal of KIISE:Software and Applications
• /
• v.31 no.8
• /
• pp.1101-1112
• /
• 2004

In this paper, we propose a Web page weighting scheme using the user preference in each sense of query word to improve the performance of Web search. Generally search engines assign weights to a web page by using relevancy only, which is obtained by comparing the query word and the words in a web page. In the information retrieval from huge data such as the Web, simple word comparison cannot distinguish important documents because there exist too many documents with similar relevancy In this paper we implement a WordNet-based user interface that helps to distinguish different senses of query word, and constructed a search engine in which the implicit evaluations by multiple users are reflected in ranking by accumulating the number of clicks. In accumulating click counts, they are stored separately according to senses, so that more accurate search is possible. The experimental results with several keywords show that the precision of proposed system is improved compared to conventional search engines.

• The Journal of the Acoustical Society of Korea
• /
• v.26 no.1E
• /
• pp.21-26
• /
• 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.

• MALSORI
• /
• no.59
• /
• pp.53-68
• /
• 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.

• Korean journal of food and cookery science
• /
• v.22 no.2 s.92
• /
• pp.213-221
• /
• 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.

• The Journal of the Korea Contents Association
• /
• v.16 no.10
• /
• pp.687-692
• /
• 2016

Conventional way to search documents is keyword-based queries using vector space model, like tf-idf. Searching process of documents which is based on keywords can make some problems. it cannot recogize the difference of lexically different but semantically same words. This paper studies a scheme of document search based on document queries. In particular, it uses centrality vectors, instead of tf-idf vectors, to represent query documents, combined with the Word2vec method to capture the semantic similarity in contained words. This scheme improves the performance of document search and provides a way to find documents not only lexically, but semantically close to a query document.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.4
• /
• pp.299-303
• /
• 2009

Patent information search is used for checking existence of earlier works. In patent information search, there are many reasons that fails to get appropriate information. This research proposes a method extracting alternative word candidates in order to minimize search failure due to keyword mismatch. Assuming that two words have similar meaning if they have similar co-occurrence words, the proposed method uses the concept of concentration, association word set, cosine similarity between association word sets and a ranking modification technique. Performance of the proposed method is evaluated using a manually extracted alternative word candidate list. Evaluation results show that the proposed method outperforms the document vector space model in recall.

• Knowledge Management Research
• /
• v.15 no.3
• /
• pp.141-168
• /
• 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
• /
• v.10 no.2
• /
• pp.33-41
• /
• 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.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.960-968
• /
• 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.