• International Commerce and Information Review
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• v.6 no.3
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• pp.159-180
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• 2004

This paper focus on building online trust in electronic commerce between partner that have never traded with each other before, the so-called first trade situation. For this, this paper proposes the model to build trust for conduction first trade transaction in e-trade(so-ca1led Trust Matrix Model : TMM). The TMM is based on the idea that for business to business electronic trade a balance has to be found between anonymous procedural trust, i.e. procedural solutions for trust building, and personal trust based on positive past experiences within for first trade situation, because of the lack of experience in these situations. The procedural trust solutions are related in the notion of institution-based trust, because the trust in the procedural solutions depends on the trust one has in the institutions that issued or enforces the procedure. The TMM can be used as a tool to analyze and develope trust-building services to help organizations conduct first -trade electronic trade.

• Journal of the Korean Mathematical Society
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• v.54 no.2
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• pp.613-626
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• 2017

For the overdetermined linear system, when both the data matrix and the observed data are contaminated by noise, Total Least Squares method is an appropriate approach. Since an ill-conditioned data matrix with noise causes a large perturbation in the solution, some kind of regularization technique is required to filter out such noise. In this paper, we consider a Dual regularized Total Least Squares problem. Unlike the Tikhonov regularization which constrains the size of the solution, a Dual regularized Total Least Squares problem considers two constraints; one constrains the size of the error in the data matrix, the other constrains the size of the error in the observed data. Our method derives two nonlinear equations to construct the iterative method. However, since the Jacobian matrix of two nonlinear equations is not guaranteed to be nonsingular, we adopt a trust-region based iteration method to obtain the solution.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.241-248
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• 2018

A Recommender System (RS) is a system that provides optimized information to users in an over-supply situation. The key to RS is to accurately predict the behavior of the user. The Matrix Factorization (MF) method was used for this prediction in the early stage, and according to the recent SNS development, social information is additionally utilized to improve prediction accuracy. In this paper, we use RS internal trust cluster, which was overlooked in previous studies, to further improve performance and analyze the characteristics of trust clusters.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.1-23
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• 2005

The decentralized static output feedback design problem is considered. A constrained trust region method is developed that solves this optimal control problem when a complete set of state variables is not available. The considered problem is interpreted as a non-linear (non-convex) constrained matrix optimization problem. Then, a decentralized constrained trust region method is developed for this problem class exploiting the diagonal structure of the problem and using inexact computations. Finally, numerical results are given for the proposed method.

• Archives of Plastic Surgery
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• v.47 no.6
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• pp.567-573
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• 2020

Background Polyurethane implants have been used on and off in breast reconstruction since 1991 while prepectoral breast reconstruction has gained popularity in recent times. In this study, we present our outcomes from the use of acellular dermal matrix (ADM) complete wrap with polyurethane implants in prepectoral breast reconstruction. Methods This is a retrospective review of prospectively maintained database from 41 patients receiving complete ADM wrap with prepectoral polyurethane implants over a 3-year period. Selection criteria were adapted from a previous study (4135 Trust Clinical Audit Database) evaluating prepectoral reconstruction with Braxon matrices. Patient demographics, operative data, surgical complications, and outcomes were collected and analyzed. Results A total of 52 implant reconstructions were performed in 41 patients with a mean follow-up of 14.3 months (range, 6-36 months). The overall reported complication rates including early (less than 6 weeks) and late complications. Early complications included two patients (4.9%) with wound dehiscence. One of which had an implant loss that was salvageable. Another patient (2%) developed red-breast syndrome and two women (4.9%) developed with seroma treated conservatively. Late complications included one patient (2%) with grade II capsular contraction, 12 patients with grade I-II rippling and two patients (4.9%) with grade III rippling. Conclusions We present our experience of prepectoral polyurethane implant using complete ADM wrap. This is one of the few papers to report on the outcome of the prepectoral use of polyurethane in immediate implant-based breast reconstruction. Our early observational series show satisfactory outcome and long-term results are warranted by a large multicenter study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2082-2102
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• 2018

In recent years, social network related applications such as WeChat, Facebook, Twitter and so on, have attracted hundreds of millions of people to share their experience, plan or organize, and attend social events with friends. In these operations, plenty of valuable information is accumulated, which makes an innovative approach to explore users' preference and overcome challenges in traditional recommender systems. Based on the study of the existing social network recommendation methods, we find there is an abundant information that can be incorporated into probability matrix factorization (PMF) model to handle challenges such as data sparsity in many recommender systems. Therefore, the research put forward a unified social network recommendation framework that combine tags, trust between users, ratings with PMF. The uniformed method is based on three existing recommendation models (SoRecUser, SoRecItem and SoRec), and the complexity analysis indicates that our approach has good effectiveness and can be applied to large-scale datasets. Furthermore, experimental results on publicly available Last.fm dataset show that our method outperforms the existing state-of-art social network recommendation approaches, measured by MAE and MRSE in different data sparse conditions.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.961-969
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• 2021

The study aimed to investigate the level of customer trust, loyalty, and re-purchase intention of coastal tourism in Thailand during the COVID-19 crisis; to test the different levels of customer trust, loyalty, and re-purchase intention by local tourists between East-side and West-side coasts of Thailand; and to examine the influence of customer trust and loyalty on re-purchase intention in coastal tourism. Using multistage sampling, this study sampled 487 Thai local tourists who had experienced coastal tourism in Thailand during the COVID-19 crisis. A questionnaire, descriptive analysis, independent sample t-test, correlation matrix, and multiple regression analysis were used to collect and analyze the data. All customer trust, loyalty, and re-purchase intentions in coastal tourism by local tourists were at a high level. There were significantly different levels of customer trust, loyalty, and re-purchase intentions by local Thai tourists between the East-side and West-side coasts of Thailand. Moreover, the study found that there was a significant positive influence of customer trust and loyalty on re-purchase intentions in coastal tourism by local tourists in Thailand during the COVID-19 crisis. This study indicates that Thai tourism industry still must develop and improve its local customer loyalty and trust because these positively influence customer re-purchase intentions.

• Journal of Mechanical Science and Technology
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• v.16 no.2
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• pp.203-210
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• 2002

For effective response surface modeling during sequential approximate optimization (SAO), the normalized and the augmented D-optimality criteria are presented. The normalized D-optimality criterion uses the normalized Fisher information matrix by its diagonal terms in order to obtain a balance among the linear-order and higher-order terms. Then, it is augmented to directly include other experimental designs or the pre-sampled designs. This augmentation enables the trust region managed sequential approximate optimization to directly use the pre-sampled designs in the overlapped trust regions in constructing the new response surface models. In order to show the effectiveness of the normalized and the augmented D-optimality criteria, following two comparisons are performed. First, the information surface of the normalized D-optimal design is compared with those of the original D-optimal design. Second, a trust-region managed sequential approximate optimizer having three D-optimal designs is developed and three design problems are solved. These comparisons show that the normalized D-optimal design gives more rotatable designs than the original D-optimal design, and the augmented D-optimal design can reduce the number of analyses by 30% - 40% than the original D-optimal design.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.467-477
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• 2017

The purpose of this study is to present the ranking and difference of the importance and satisfaction of the swimsuit brand image that the swimsuit consumers perceive through the IPA and to provide the implications for the activation of the swimsuit industry market by diagramming it with the IPA matrix. To do this, we analyzed 298 questionnaire data for university students and graduate students and the following conclusions were drawn through the ranking of importance, satisfaction, and the corresponding sample t-test and IPA matrix. First, as a result of examining the ranking through the average of the importance and satisfaction of the swimsuit brand image, the importance showed average 4.0 or more in order of 'quality', 'functionality', 'price', 'design', 'trust', 'color', and the satisfaction showed average 3.5 or more in order of 'trust', 'quality', 'functionality' and 'awareness'. Second, as a result of difference verification in swimsuit brand image, it showed a significant difference in order of 'quality', 'price', 'functionality', 'design', 'color', 'trust', 'sophistication'. On the other hand, it showed no significant difference of cognitive images. Third, as a result of IPA of swimsuit brand image, the factors of 'design', 'functional', 'quality' and 'trust' were included in I quadrant, 'price' 'color' in II quadrant, 'advertising image', 'event', 'popularity', 'originality' in III quadrant, and 'awareness', 'sophistication' in IV quadrant.