• Science of Emotion and Sensibility
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• v.12 no.2
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• pp.215-224
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• 2009

This research extracted emotional words for measuring user's emotions expressed while using a cellular phone and a web. And then the emotional words were compared to find out whether the difference in emotional words by the type of products. The results of this study suggest that the hardware-oriented products used for specific purpose such as cellular phone extracted a lot of emotional words related to 'Satisfaction in Usability' and 'Pleasure'. 'Satisfaction in Usability' are conceived satisfying in usability or practicality of product. 'Pleasure' are pleasant emotions expressed while using a product. However the emotional words related to 'Aesthetics' and 'Novelty' were omitted. 'Aesthetics' are expressed by product's appearance and by various visual information while using a product. 'Novelty' are expressed by something that is novel and new that has never been experienced. On the other hand the software-oriented products used rather to find something better and new information than to perform specific tasks such as web extracted a lot of emotional words related to 'Novelty'. Therefore, the results of this research have found evidence that it is desirable to make a set of subjective evaluation scale by the type of products. When making the subjective evaluation scale, it is important to use appropriate emotional words for the purpose of use and the characteristics of those products.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.133-139
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• 2019

This study investigated what type of destination image on Facebook increases positive attitude towards the destination and desire to travel. More specifically, we hypothesized that Facebook user's public self-consciousness will moderate the advertising effect, so we tested the difference in attitude towards the destination and willingness to visit and recommend based on the type of destination image (presence/absence of a tourist in the image) and user's public self-consciousness (high/low). As a result, when a tourist was present in the photo, positive attitude toward destination was higher than when absent. In addition, the high public self-consciousness group showed greater attitude toward destination and higher willingness to visit and recommend than low public self-consciousness group when a tourist was present in the image. However, the low public self-consciousness group showed no difference depending on the presence of a tourist. This research not only is academically significant as it experimentally tested advertising effect depending on type of destination image and Facebook user's public self-consciousness, but also holds practical significance as it suggests advertisement method to consider consumer's predisposition.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.193-198
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• 2008

Now days with the significant grows of internet and PC hardware/software, most of the business, include small size business and soho, is worked by PC/Network. However these critical changes, all the PC/Network system or service is identical and these make user a inconvenient experience and unpleasant aspect to PC/Network. This research is to segment the user bases on the user context of using PC and all this segmentation was done in contextual design process. From Contextual Interview, all data of user context of environment, use case, needs, voices, break downs were captured and these data were interpreted and filtered to affinity notes and user modeling. After the interpretation, the consolidation was being held by affinity diagram, which could make ready to segment the user. For the result, 3 segmentation was evaluated and which are the 'Holistic Networker', 'Unplugged Controller' and 'Passive Reactor'.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.625-632
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• 2006

Collaborative filtering is a technology that aims at teaming predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn't rate by a new user based on the values that the nearest neighbors rated items.

• Journal of Industrial Convergence
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• v.18 no.2
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• pp.95-100
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• 2020

Among the customer-oriented data used to comprehend the customer, the user review data has received much attention as it provides insights into customer opinion in a detailed and large-scale manner; many customers have come to rely upon and trust the user reviews. Many application developers are cognizant of the importance of user reviews, so they monitor and respond to these reviews. However, due to the absence of a systematic method, developers have been investing their time and money without clear correlation to the customer satisfaction. Therefore, this paper suggests a systematic method to select user reviews from the application market using the Kano Model that deals with customer satisfaction and service quality, thereby maximizing the customer satisfaction under the given time period and budget. This method is constructed in the following phases: the user review collection and requirement elicitation phase in which the developers collect user reviews from the application market and elicit requirements, the Kano Model application and selection phase in which the Kano Model is applied to the elicited requirements and selection occurs based on the quality type, and the stakeholder review and redefinition phase in which relevant personnel gather to review and redefine requirements from an internal perspective.

• Journal of The Korean Association of Information Education
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• v.16 no.4
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• pp.397-407
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• 2012

According to the fast supply of the SMART device, many people have a lot of interests about SMART education. The Korean government is showing a lot of effort towards the promotion of SMART education. In this study, I developed a behavioral assessments and QR code based mobile web application for school education. The developed system works seamlessly across all popular mobile device platforms and if you save the data after the assessment, this system will automatically create each person's mobile web page. Students and teachers can add comments to this page and can improve the accessibility for finding the address of this page to QR code. The developed system can contribute to the class members understanding the personality of each other and make communication run smoothly. As a result of application of developed system, it is expected to contribute to increasing the interaction between a students, parents and teachers because the developed system provides a service based to education consumer's TPO(Time, Place, Occasion).

• Archives of design research
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• v.20 no.3 s.71
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• pp.203-214
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• 2007

This study proposes an information retrieval method reflecting the user's preferences based on the fuzzy set theory to develop information contents which support pedestrian's navigation. Firstly, the research evaluated subjects' preferences on commercial spaces set to a hypothetical destination. Also it surveyed the causal relationship between the visual characteristics and the emotional characteristics to propose methods of Navigation Knowledge Base (NKB). The NKB was composed of three elements; 1. the correlation model between emotional characteristics, 2. the causal relationship between visual characteristics and emotional characteristics, 3. the transformation model between visual characteristics and the physical characteristics. Secondly, this study classified the pedestrian's destination search into 4 types with his or her preferences and the time conditions limited during navigation. For each type it presented the Destination Search Algorithm (DSA). Finally, the research simulated the destination search in 4 navigation types using NKB and DSA and verified the availability of the information retrieval method reflecting pedestrian's preferences. In conclusion, the proposed information search method will be applied to reflect the user's preferences to develop information appliances.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.600-608
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• 2017

Recently data growth rates are growing exponentially according to the rapid expansion of internet. Since users need some of all the information, they carry a heavy workload for examination and discovery of the necessary contents. Therefore information retrieval must provide hierarchical class information and the priority of examination through the evaluation of similarity on query and documents. In this paper we propose an Multi-class support vector machines model based clustering for hierarchical document categorization that make semantic search possible considering the word co-occurrence measures. A combination of hierarchical document categorization and SVM classifier gives high performance for analytical classification of web documents that increase exponentially according to extension of document hierarchy. More information retrieval systems are expected to use our proposed model in their developments and can perform a accurate and rapid information retrieval service.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.19-29
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• 2020

As the AI speaker market is growing rapidly in recent years, the competition for the preoccupation of children who are the main users and the future prospective customers of the related companies is very intense. However, there is a lack of empirical research on how children interact with AI speakers. Therefore, this research examines the interactions between children and AI speakers, primarily through field studies, to extract what functions they use and what features they have. For this purpose, 799 conversations were collected and analyzed using the log data of the AI speaker recorded in real time. As a result, children were more likely to use children's songs, fairy tales, emotional conversations, and personification compared to adults. In addition, content analysis by specific types resulted in success/failure cases of interaction between children and AI speakers and proposed improvements by failure type. This study is meaningful in that it identifies children's AI speaker preferences, content, and major conversation patterns, and provides guidelines for developing services that meet children's eye level.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.226-235
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• 2009

People use various information for searching POI in the navigation system such as name, category, address, phone number. Most of users use name and category to search their POT. They don't know exact name in POI DB provided by Maker. They use abbreviated or generalized name as key word for searching POI. Because of these reasons, the hit ratio has been very low. In this paper, We suggest a extra DB_construction system for raising the hit ratio. It generates allomorphes DB link to the POI name in original DB. We classified the POI names in original DB into seven types of allomorph by analyzing the gathered patterns from the POI DB which has over 650,000 entries. For auto_generating the allomorphes, we made 577 rules based on the classified types. And we generated the allomorphes manually for the entries which are difficult to make the rule and has low frequency The generated allomorphes account for 35.8% of all original DB. The hit ratio is 89% under suggested system.