• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1570-1594
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• 2017

Social network services have recently changed from environments for simply building connections among users to open platforms for generating and sharing various forms of information. Existing user reputation computation methods are inadequate for determining the trust in users on social media where explicit ratings are rare, because they determine the trust in users based on user profile, explicit relations, and explicit ratings. To solve this limitation of previous research, we propose a user reputation computation method suitable for the social media environment by incorporating implicit as well as explicit ratings. Reliable user reputation is estimated by identifying malicious information raters, modifying explicit ratings, and applying them to user reputation scores. The proposed method incorporates implicit ratings into user reputation estimation by differentiating positive and negative implicit ratings. Moreover, the method generates user reputation scores for individual categories to determine a given user's expertise, and incorporates the number of users who participated in rating to determine a given user's influence. This allows reputation scores to be generated also for users who have received no explicit ratings, and, thereby, is more suitable for social media. In addition, based on the user reputation scores, malicious information providers can be identified.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.83-90
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• 2010

We describe a novel method for improving the performance of the UCC retrieval using content reputation and user reputation. The UCC retrieval is a part of the information retrieval. The goal of the information retrieval system finds documents what users want, so the goal of the UCC retrieval system tries to find UCCs themselves instead of documents. Unlike the document, the UCC has not enough textual information. Therefore, we try to use the content reputation and the user reputation based on non-textual information to gain improved retrieval performance. We evaluate content reputation using the information of the UCC itself and social activities between users related with UCCs. We evaluate user reputation using individual social activities between users or users and UCCs. We build a network with users and UCCs from social activities, and then we can get the user reputation from the network by graph algorithms. We collect the information of users and UCCs from YouTube and implement two systems using content reputation and user reputation. And then we compare two systems. From the experiment results, we can see that the system using content reputation outperforms than the system using user reputation. This result is expected to use the UCC retrieval in the feature.

• Journal of KIISE
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• v.43 no.1
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• pp.96-105
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• 2016

Recently, social network services have changed by moving towards an open platform where, as well as simply allowing the building of relationships among users, various types of information can be generated and shared. Since existing user reputation management methods evaluate user reliability based on user profiles, explicit relations, and evaluation, they are not suitable for determining user reliability on social media due to few explicit evaluation. In this paper, we analyze social activities on social media and propose a new user reputation management method that considers implicit evaluation as well as explicit evaluation. The proposed method derives positive and negative implicit evaluation from social activities, and generates user reputation information by field in order to consider user expertise. It also considers the number of users that participate in evaluation in order to measure user influence. As a result, it generates the reputation information of users who have no explicit evaluation and creates user reputation information that is more suitable for social media.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.273-277
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• 2009

Recently user-generated contents have been drastically increased. In this paper, we introduce the user reputation which can be used to evaluate quality of the content they created. First we have composed a social network that is based on user activity. And we have developed the algorithm to evaluate the users' reputation using this social network.

• Management & Information Systems Review
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• v.32 no.3
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• pp.173-188
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• 2013

I guess that e-learning buying behaviors have some selection criteria which are interactivity, contents quality, perceived usefulness, and corporate reputation. And I propose that the effects of selection criteria on adaptation intention are moderated by buyer-user status. To accomplish research purpose, I conducted a field survey with a self-administered questionnaire in both user-buyer and analysed the causal relations among the variables by regression analysis. Findings are these. In all of the customer, interactivity, perceived usefulness, and corporate reputation had positive effects on re-attending intention to another e-learning class. In case of users(students), interactivity, contents quality, and perceived usefulness had positive effects, but buyer(parents) showed both perceived usefulness and corporate reputation had positive effects on re-attending intention.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.2
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• pp.35-47
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• 2004

Though P2P network systems are widely used, not so much research has been done on security issues. One of the serious problem in P2P network is the authentication problem. To resolve this problem, we introduce a new concept, a "self-reputation system" in which a user manages her own reputation. We define self-reputation systems by presenting several requirements. We also give one instance of self-reputation system. The proposed instance satisfies the requirement including the prevention of erasing and the platform independence.ependence.

• The Journal of Information Systems
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• v.14 no.2
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• pp.155-172
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• 2005

Securities companies which faced with severe competition should not only attract new customers but also retain their on-line customers. This study examines the factors affecting loyalty of on-line stock trading customers. The research model based on the previous studies was established and the research hypotheses were generated. The test results based on the data gathered from 87 users of on-line stock trading services show that user satisfaction, learning cost, transaction fees, and reputation influence customer loyalty. User satisfaction, learning cost and reputation are positively related to customer loyalty, whereas transaction fee is negatively related to customer loyalty. The results also support that information quality and system quality are positively related to user satisfaction. The hypothesis that transaction fee is related to user satisfaction is not supported. There is no significant information to say that security risk is related to user satisfaction. It is considered that the study results may help managers to increase customer retention.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.459-476
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• 2008

The user needs to find the answer to your question is growing fast at the service using collective intelligent knowledge. In the previous researches, it was proven that the non-text information like view counting, referrer number, and number of answer is good in evaluating answers. There were also many works about evaluating answers using the various kinds of word dictionaries. In this work, we propose new method to evaluate answers to question effectively using user reputation that estimated by the social activity. We use a modified PageRank algorithm for estimating user reputation. We also use the similarity between question and answer. From the result of experiment in the Naver GisikiN corpus, we can see that the proposed method gives meaningful performance to complement the answer selection rate.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.235-242
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• 2013

Reputation systems aggregate users' feedback after the completion of a transaction and compute the "reputation" of products, services, or providers, which can assist other users in decision-making in the future. With the rapid growth of online e-Learning content providing services, a suitable reputation system for more credible e-Learning content delivery has become important and is essential if educational content providers are to remain competitive. Most existing reputation systems focus on generating ratings only for user reputation; they fail to consider the reputations of products or services(item reputation). However, it is essential for B2C e-Learning services to have a reliable reputation rating mechanism for items since they offer guidance for decision-making by presenting the ranks or ratings of e-Learning content items. To overcome this problem, we propose a novel collaborative filtering based reputation rating method. Collaborative filtering, one of the most successful recommendation methods, can be used to improve a reputation system. In this method, dual information sources are formed with groups of co-oriented users and expert users and to adapt it to the reputation rating mechanism. We have evaluated its performance experimentally by comparing various reputation systems.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.68-79
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• 2021

On the Social Internet of Things (SIoT), social activities occur through which the vehicle generates a variety of data, shares them with other vehicles, and sends and receives feedbacks. In order to share reliable information between vehicles, it is important to determine the reliability of a vehicle. In this paper, we propose a vehicle trust evaluation scheme to share reliable information among vehicles. The proposed scheme calculates vehicle trust by considering user reputation and network trust based on inter-vehicle social behaviors. The vehicle may choose to scoring, ignoring, redistributing, etc. in the social activities inter vehicles. Thereby, calculating the user's reputation. To calculate network trust, distance from other vehicles and packet transmission rate are used. Using user reputation and network trust, local trust is calculated. It also prevents redundant distribution of data delivered during social activities. Data from the Road Side Unit (RSU) can be used to overcome local data limitations and global data can be used to calculate a vehicle trust more accurately. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.