• 한국의류학회지
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• 제30권6호
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• pp.928-938
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• 2006

Tween refers 'between' generation aged of 10-16 who are between child and high teens. Tweens have been regarded as one of the fast growing markets and they show some unique characteristics as next-generation consumers. This study has explored the perception of their appearances and clothing styles, style preferences, and influencing factors on clothing behavior of tweens. 120 students aged of 12-15 participated in depth interview and data were structured and categorized by applying domain analysis. Results showed tweens have great concerns on their appearances and styles, so they want to express their identities and aesthetic favors through clothing styles. As for clothing style preferences, they have multi-facet tastes such as casual, active, feminine/ masculine, sophisticated, dramatic, etc. Factors influencing on their clothing behavior include reference groups like friends, dual desires of conformity and individuality, fashion, and brand names.

• Journal of Computing Science and Engineering
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• 제6권4호
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• pp.243-256
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• 2012

In this paper we present a framework for a novel kind of context-aware preference query composition whereby queries for the Preference SQL system are created. We choose a commercial e-business platform for outdoor activities as a use case and develop a context model for this domain within our framework. The suggested model considers explicit user input, domain-specific knowledge, contextual knowledge and location-based sensor data in a comprehensive approach. Aside from the theoretical background of preferences, the optimization of preference queries and our novel generator based model we give special attention to the aspects of the implementation and the practical experiences. We provide a sketch of the implementation and summarize our user studies which have been done in a joint project with an industrial partner.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• 한국주거학회논문집
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• 제9권3호
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• pp.133-144
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• 1998

The purpose of this study is to analyze housing expectations and housing preferences of college students' first purchased house for the future. Data was collected through self-administered questionnaires for this study and the samples consisted of 797 college students. This study found that college students preferred a high-rise apartments, a 20-24 pyung (66.0㎡-79.2㎡) house in housing size and relatively new house. The housing features and interior features that college students like to have and expected to have in the first residence were different. Their housing expectations were fairly realistic considering their affordability. College students preferred to furnish video and audio equipments, exercise equipments and ceiling fans. They generally preferred hardwood floorings and decorative moldings and expected to have built-in closet in their master bedrooms. They expected to have linen closets and large mirrors in their bathrooms. They preferred ceramic tile counter tops, wood cabinets, vinyl floorings in their kitchens and they also preferred to use kitchen as a utility.

• 복식
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• 제50권8호
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• pp.43-55
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• 2000

The objectives of this study were to investigate the differences of image evaluation of baseball uniform by uniform design and perceiver's gender. and to examine how baseball uniform preferences vary according to perceiver's gender. Stimuli consisted of 12 color photographs of a male model wearing a baseball uniforms which were manipulated according to the color of shorts and pants. A semantic differential scale of 23 items were used to evaluate the image of the stimuli. Subjects were 288 males and females. Five dimensions derived to account for the image of baseball uniform. These were manly, ability, activity, preference, and visibility. Wearing of red shirts had a positive effect on the evaluation of ability, activity, and visibility. Dark blue shirts had a positive effect on the evaluation of preference. Grey uniforms had negative effects on the evaluation of ability, activity, and visibility. Men liked white uniforms and vertical stripes uniforms of black and white more than dud women. Women talked dark blue shirts more than did men.

• 정보통신설비학회논문지
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• 제4권1호
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• pp.23-29
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• 2005

This study is about designing mobile phone display in consideration of elder people's preferences by reason of their optical weakness. The research is closely connected with designing user-friendly interface by considering user characteristics. The criteria for first experiment are font sizes, font types, line spacing and background colors. With the experiment result, relative importance of each attribute and subjective preference are investigated by conjoint analysis. Secondly, an optimal display design for elder people is presented by response surface method on the basis of the result of conjoint analysis, other statistical analyses, and user interviews.

• 응용통계연구
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• 제20권3호
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• pp.441-447
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• 2007

컨조인트 분석은 경영학에서 제품 대안들에 대한 소비자의 선호 정도로부터 소비자가 각 속성에 부여하는 상대적 중요도와 각 속성수준의 효용 부분가치를 추정하는 분석방법이다. 본 논문에서는 초등학교 컴퓨터 특기 적성 교육 과목들에 대한 학생들의 선호정도를 컨조인트 분석을 통해 알아보았다. 그 과정에서 특별히 인공 신경망 분석을 부가적으로 수행하면 보다 많은 유용한 지식을 얻을 수 있음을 중점적으로 다루었다.

• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.142-148
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• 2019

Relatively few countries provide separate bicycle lanes for cyclists. Hence, tools for suggesting cycling routes are essential for a safe and pleasant cycling experience. This study aims to develop a mobile application to build cycling routes based on user preferences, specifically location, search radius, ride distance, and number of optimal routes. Our application calls the Strava API to retrieve Strava cycling segments crowdsourced from the cycling community. Then, it creates a graph consisting of the start and end points of these segments. Beginning from a user-specified location, the depth-first search algorithm (DFS) is applied to find routes that conform to the user's preferences. Next, a set of optimal routes is obtained by computing a trade-off ratio for every discovered route. This ratio is calculated from the lengths of all segments and the lengths of all connecting paths. The connected routes can be displayed on a map on an Android device or exported as a GPX file to a bike computer. Future work must be performed to improve the design of the user interface and user experience.

• 한국주거학회논문집
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• 제22권6호
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• pp.97-106
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• 2011

Due to the belief that housing for university students is uniform materialistic and non-human, a variety of needs and preferences of the residents is becoming more important. In order to more positively identify the user's emotional needs, a design that meets the needs of the resident's living space is absolutely necessary. The purpose of this study is to group the sensibility types of university students, to analyze the preferred interior image and interior design elements, and then to understand the preferred characteristics according to sensibility type. To collect the data, a survey method was used for 224 university students in Seoul and the metropolitan area. The data analysis used SPSS PC 14.0 to find the frequency, t-test, chi-square, factor analysis, and cluster analysis. The results of the analysis are as follows: First, sensibility types were segmented into four groups including 'Unique & Elegant', 'Static & Tidy', 'Warm & Comfortable', and 'Characteristic & Free'. Second, the preferred interior image was the Minimal-Dandy-Natural Image, the preferred floor_material was wood, and the preferred wall_material was marble. Third, there were different interior preferences according to each type of gender & sensibility.

• 한국의상디자인학회지
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• 제6권2호
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• pp.135-144
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• 2004

The purpose of this study was to determine cosmetic preferences and purchasing behavior of two segmented groups(career women vs. college females). We employed questionnaires and collected data from 443 people. Only 400 reliable questionnaires were used for a statistical analysis with SPSS program on the frequency, Chi-square test, and t-test. The results of this study were as follows: 1. The amount of makeup, and the reason for makeup were statistically associated with the segmented groups which are career women and college females. Also we realized that there was a significant difference in 'sophisticated' and 'sexy' makeup images. Both images were more highly sought in career women. 2. The preference of lip colors & textures, types of eye liner, and types of foundation were associated with the segmented career/college female groups. Both groups favored more glossy lipstick. Career women preferred liquid eye liner and foundation, while college females like to use no foundation. 3. Both career women and college females acquired information about cosmetics from the friend's advice rather than TV or magazine. In purchasing, career women considered the most quality, while college females considered suitable cosmetics for their natural skin types.