• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.589-595
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• 2019

Recently, the mobile social networks, which consider both social relationship between users and mobile communication networks, have been received great attention. In this paper, we consider the trust degree of node as the social relationship for the cooperative communication networks. In contrast to the existing works that consider the case of the perfect trust degree information, for the case that transmitter has an imperfect trust degree information, we propose an imperfect trust degree based cooperative communication technique that maximizes a throughput. We first model the imperfect trust degree information as a probability distribution and derive the outage probability using the probability distribution. Then, we propose the transmission scheme that maximizes the throughput, which consider both outage probability and transmission rate. The simulation results show that the proposed cooperative transmission scheme outperforms the conventional scheme in terms of the throughput.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3766-3788
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• 2017

A blockchain is an underlying technology and basic infrastructure of the Bitcoin system. At present, blockchains and their applications are developing rapidly. However, the basic research of blockchain technology is still in the early stages. The efficiency and reliability of blockchain communication is one of the research problems that urgently need to be studied and addressed. Existing algorithms may be less feasible for blockchain-based communication because they only consider a single communication factor (node communication capability or node trust degree) and only focus on a single communication performance parameter(communication time or communication reliability). In this paper, to shorten the validation time of blockchain transactions and improve the reliability of blockchain-based communication, we first establish a multi-link concurrent communication model based on trust degree, and then we propose a novel integrated factor communication tree algorithm (IFT). This algorithm comprehensively considers the node communication link number and the node trust degree and selects several nodes with powerful communication capacity and high trust as the communication sources to improve the concurrency and communication efficiency. Simulation results indicate that the IFT algorithm outperforms existing algorithms. A blockchain communication routing scheme based on the IFT algorithm can increase communication efficiency by ensuring communication reliability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.509-515
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• 2017

In this paper, for a cooperative communication system with multiple relays, we consider a relay selection method by exploiting the trust degree information of relay nodes. In the cooperative communication system, we interpret the trust degree of relays as the probability that relay helps the communication between the transmitter and receiver. We first provide an expected achievable rate at the receiver by taking into account the both cases that the relay helps the transmission of transmitter and the relay does not help the transmission of transmitter according to its trust degree. For given trust degree information, we propose an efficient relay selection method to maximize the expected achievable rate at the receiver. For the various configurations, the simulation results confirm that the proposed relay selection method outperforms the conventional relay selection method, which does not consider the trust degree of relay nodes.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.1
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• pp.61-71
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• 2012

Other-regarding preferences (such as trust, reciprocity and altruism) between companies, between consumers and retailers, and between employers and employees are integral elements in determining economic performance. Social trust which is a core element of social capital, especially, is known to reduce transaction costs, help solve collective action problems, and contribute to economic, social, and political development. Therefore, social trust has been given a great deal of attention across an array of academic disciplines for its role in promoting cooperation among individuals and groups, and for its positive influence on economic performance. Most studies describe Korea as a low-trust society than Japan or China. To identify the causes of social trust, this paper focuses on differences of social values (perception on competition and wealth accumulation) in 4 countries (Japan, China, Korea, and United States). Based on World Values Survey data, this paper analyzes effects of the social values on social trust. Social trust was measured by degree to which a respondent thinks that most people can be trusted. Perception on competition was measured by the degree to which a respondent thinks that competition is harmful, and perception on wealth accumulation was done by the degree to which a respondent thinks that wealth can grow so there is enough for everyone. The results showed that social trust was affected by perception on competition and wealth accumulation. A respondent showed higher level of social trust when he (or she) perceived positively competition and wealth accumulation. For enhancing social trust in a country, it is not easy to reduce income inequality and corruption which were reported as causes of social trust by previous studies. Compared to them, social values can be changed more easily by various concrete measures like education and mass-media. Differently from previous studies this paper stresses the concrete measures to enhance social trust in a country.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.169-178
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• 2019

Due to the characteristics of an ad hoc network without a control center, self-organization, and flexible topology, the trust evaluation of the nodes in the network is extremely difficult. Based on the analysis of ad hoc networks and the blockchain technology, a blockchain-based node-level trust evaluation model is proposed. The concepts of the node trust degree of the HASH list on the blockchain and the perfect reward and punishment mechanism are adopted to construct the node trust evaluation model of the ad hoc network. According to the needs of different applications the network security level can be dynamically adjusted through changes in the trust threshold. The simulation experiments demonstrate that ad-hoc on-demand distance vector(AODV) Routing protocol based on this model of multicast-AODV(MAODV) routing protocol shows a significant improvement in security compared with the traditional AODV and on-demand multipath distance vector(AOMDV) routing protocols.

• Journal of Korean Society for Quality Management
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• v.31 no.2
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• pp.51-68
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• 2003

This research addresses familiarity and degree of using Internet site and trust building to affect service quality in e-commerce. We assume that the familiarity and frequency of using Internet site affect perceived service quality of customer. Also it assumes that customer's trust building intermediates their relationships. Several hypotheses are set to test these assumptions empirically. Consequently, the results show that familiarity of using Internet site affects customer's perceived service quality significantly except for tangible. Also it is found that degree of customer's trust building intermediates the relationship between familiarity of using Internet site and customer's perceived service quality significantly. For gaining good trust of customer in Internet site, we suggest that familiarity should fit into customer's needs.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.291-299
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• 2020

The purpose of this study focuses on finding the influences of social capital on personal happiness by studying service industry employees. This current study utilized a questionnaire survey method which was used to collect the analysis data, from July 20 to August 10 in 2019. 281 questionnaires were gathered in Korea and the statistical analysis was conducted. This study recognized social capital as 3 independent variables and personal happiness as a dependent variable. Social capital is composed of the social network, social trust and social norms. First, the social network consisted of the satisfaction degree in one's social relations and the social interaction degree. Second, social trust is composed of the trust degree with ones neighbors in the same local area. Last, social norms are consisted of reciprocity, participation and a sense of belonging one feels in the same society. The findings of this study were as follows: first, it was found that social network, social trust, and social norms made affirmative influences on personal happiness of the employees. Second, the social network and social norms made statistically significant influences with personal happiness, but social trust was shown to not have similar influence.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.35-45
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• 2014

Due to the spread of unreliable online information on the social network services, the users are faced with a difficult problem for determining if the information is trustworthy or not. At present, the users should make a decision by themselves throughly for the trustworthiness of the information. Therefore, we need a way to systematically evaluate the trustworthiness of information on the social network services. In this paper, we design a trust index, called KTI (Korean Trust Index for SNS), as a criterion for measuring the trust degree of the information on the social network services. Using KTI, the users are readily able to determine whether the information is trustworthy. Consequently, we can estimate the social trust degree based on the variation of KTI. This paper derives the various factors affecting trust from the properties of the social network services, and proposes a model to evaluate the trustworthiness of information that is directly produced and distributed over the online network. Quantifying the trust degree of the information on the social network services allows the users to make efficient use of the social network.

• 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 Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.