• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.33-48
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• 2022

This study was performed to compare the color emotion and preference of Koreans and Chinese for a two-color combination by dyeing cotton fabric with persimmon and indigo and to establish prediction models of color preference. Nine specimens prepared by combining two different colored fabrics (persimmon and indigo) were evaluated for color emotion and preference by Korean and Chinese groups of female college students. Koreans described most specimens as natural and traditional, whereas the Chinese described them as more pleasant and elegant as well as warmer and lighter than Koreans did. The contrast tone was the most preferred combination by both groups, whereas it was perceived as more modern and less warm by Koreans. Relationships between physical color variables and color emotions were quantified; these relationships were applied to establish a prediction model of color preference with tone combination types for each group. These results could help in making the design of fashion textiles more preference- and emotion-oriented for Korean and Chinese consumers.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.803-811
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• 2010

This study analyzes the characteristics of preference ratings by dividing estimated values into four groups according to rank correlation coefficient after obtaining preference estimated value to user's ratings by using collaborative filtering algorithm. It is known that the value of standard error of skewness and standard error of kurtosis lower in the group of higher rank correlation coefficient This explains that the preference of higher rank correlation coefficient has lower extreme values and the differences of preference rating values. In addition, top n recommendation lists are made after obtaining rank fitting by using the result ranks of prediction value and the ranks of real rated values, and this top n is applied to the four groups. The value of top n recommendation is calculated higher in the group of higher rank correlation coefficient, and the recommendation accuracy in the group of higher rank correlation coefficient is higher than that in the group of lower rank correlation coefficient Thus, when using standard error of skewness and standard error of kurtosis in recommender system, rank correlation coefficient can be higher, and so the accuracy of recommendation prediction can be increased.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.659-668
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• 2007

Collaborative filtering approach for recommender systems are now widely applied in e-commerce to assist customers to find their needs from many that are frequently available. this approach makes recommendations for users based on the opinions to similar users in the system. But this approach is opened to users who present their preference to items or acquire the preference information form other users, noise in the system makes significant problem for accurate recommendation. In this paper, we analyze the relationship between the standard deviation of preference ratings for each user and the estimated ratings of them. The result shows that the possibility of the pre-filtering condition which detecting the factor of bad effect on the prediction of user's preference. It is expected that using this result will reduce the possibility of bad effect on recommender systems.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.2
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• pp.269-282
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• 1998

The visual preference of interiors focusing on lobbies was investigated as a function of six predictor variable on the base of the Informational Approach: complexity, coherence, mystery, spaciousness, brightness, plant. The Common Fcator Analysis of preference ratings yielded six common factors which helped to account for 22.3 percent of the variance in preference response to the scene. Among these factors, the factor defined as 'bright with many plants' was the most preferred and the factor defined as 'simple and closed' was the least preferred. The environmental attributes reflected in six groups of scenes were colour, resting place, window and the six predictors. In the commercial building scenes, complexity, spaciousness, coherence, brightness and mystery out of six predictors accounted for 74 percent of preference variance as the significant contributors. In the business building scenes, three predictors which are brightness, complexity, spaciousness accounted for 84 percent of preference variance. 'The amount of plant' not only influenced the preference indirectly through the intervening variable, complexity, but also was moderately correlated with brightness. The overall pattern of the resulted confirmed the usefulness of the Informational Approach to predict the preference in interiors focusing on lobbies.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.400-409
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• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.59-67
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• 2013

In this paper, we propose the LAR_CF, latent attribute rating-based collaborative filtering, that is robust to data sparsity problem which is one of traditional problems caused of decreasing rating prediction accuracy. As compared with that existing collaborative filtering method uses a preference rating rated by users as feature vector to calculate similarity between objects, the proposed method improves data sparsity problem using unique attributes of two target objects with existing explicit preference. We consider MovieLens 100k dataset and its item attributes to evaluate the LAR_CF. As a result of artificial data sparsity and full-rating experiments, we confirmed that rating prediction accuracy can be improved rating prediction accuracy in data sparsity condition by the LAR_CF.

• Journal of Global Scholars of Marketing Science
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• v.16 no.4
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• pp.75-95
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• 2006

This study attempted to determine the influence of organizational structure on the novelty preference for corporate meeting destination choice. The three dimensions of structure incorporated were formalization, centralization and complexity. A total of 75 corporate meeting planners drawn from public listed services organizations were involved. The main method of data collection was questionnaire survey and multiple regression analysis was employed as the main statistical technique. The results revealed that both formalization and centralization were negatively correlated with novelty preference while complexity was positively correlated. However, only complexity contributed significantly to the prediction of novelty preference for corporate meeting destination choice. The main implication of this study is pertaining to the segmentation and targeting of the corporate meeting market. This study helped in bridging the gap between tourism marketing and organizational research. It also contributed by developing the measurement for novelty preference from the context of experiential marketing.

• Journal of the Korean Home Economics Association
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• v.42 no.7
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• pp.19-32
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• 2004

This study examined how school-age children's peer acceptance and friendship experience were related to their psychosocial adjusment. Peer acceptance was examined in terms of sociometric status and social preference, and the friendship experience was examined in terms of the reciprocity of best friendship. The subjects were 275 children in the 4th or 5th grades. It was found that sociometric status and the reciprocity of best friendship were significant predictors of the level of loneliness that children experienced. Interaction between children's social preference score and the reciprocity of best friendship was also a significant predictor of the children's experience of loneliness. That is, the degree to which children are accepted by their peer group predicts the level of loneliness that children experience, but the strength of the prediction depends on whether the children have reciprocal best friends. Is for children's self-esteem in relation with sociometric status and the reciprocity of best friendship, only sociometric status was significant predictor of children's self-esteem. However, interaction between social preference and the reciprocity of best friendship was a significant predictor of children's self-esteem. This finding suggests that the degree to which children are accepted by their peer group predicts the level of children's self-esteem, and the strength of the prediction depends on whether the children have reciprocal best friends.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.2
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• pp.114-126
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• 2009

In this paper, a versatile multi-criteria optimization concept for fatigue life prediction is introduced. Multi-criteria decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• Journal of the Korea Safety Management & Science
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• v.12 no.1
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• pp.127-136
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• 2010

In this paper, the author proposed following two methods to improve the accuracy of the recommender system. First, in order to classify the users more accurately, the author used a EMC(Expanded Moving Center) heuristic algorithm which improved clustering accuracy. Second, the author proposed the Neighborhood-oriented preference prediction method that improved the conventional preference prediction methods, so the accuracy of the recommender system is improved. The test result of the recommender system which adapted the above two methods suggested in this paper was improved the accuracy than the conventional recommendation methods.