• 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.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.841-844
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• 2017

최근 인터넷의 급격한 발전으로 사용자의 관심사에 적합한 정보를 제공하는 추천 시스템에 대한 필요성이 증가하고 있다. 이에 따라 태그를 활용하여 추천 시스템의 성능을 향상시키려는 연구가 최근 활발하게 진행되고 있다. 하지만 태그를 활용하는 추천 시스템은 악의적인 사용자에 의해 달린 스팸 태그로 인해 부적합한 아이템을 제공한다는 문제점을 가지고 있다. 본 논문에서는 이러한 문제를 해결하기 위해 사용자 평판을 활용한 아이템 추천 기법을 제안한다. 이 기법은 먼저 사용자의 태깅 활동을 분석하여 사용자 평판을 측정한다. 다음으로 태그 네트워크를 구축한 후 사용자 평판을 고려하여 태그의 영향력을 계산하고 이를 기반으로 아이템을 추천한다.

• The KIPS Transactions:PartA
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• v.13A no.7 s.104
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• pp.599-606
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• 2006

As the P2P service does not have any administration authorities that are able to manage the behavior of participants and control the malicious users, malicious user can give harm to legitimate users for the benefit of themselves. To perform the secure transaction with new members who did not have past experiences on transaction, service users can differentiate malicious users and legitimate users by referring to the reputation information that provided by users having past experience. However, users can intentionally give false evaluation to other users on Performed transaction. We call these users as 'liar'. In this Paper, we propose a new reputation system for liar reduction to guarantee an accuracy on reputation information.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.115-120
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• 2017

Online reputation systems for social network services(SNS) aggregate users' feedback and estimate the reputation of contents or providers. The aim of this research is to enhance credibility of the online reputation system on the SNS based e-Commerce(we called it as social commerce). SNS users usually refer to evaluations from other users who bought the products before. Most social commerce sites provide reputation system to help their customer make a decision, but sometimes we can't believe the reputation because the reputation is too subjective and the seller can deceive the customer for sales promotion. Threrefore, we usually use just the average value to show the general customer's evaluation result. We applied collaborative filtering method to give more weighting to the users who have evaluated correctly in the past. As a result, we could get more accurate evaluation results by considering each customers' credibility value that was computed by collaborative filtering.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.2
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• pp.152-160
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• 2013

This paper proposes a prediction reputation system for the anti-SPIT solution in real-time VoIP environment. The existing schemes need to get the user's feedback and/or have experienced the time delay and overload as session initiates due to real-time operation. To solve these problems, the proposed scheme predicts the reputation through the statistical analysis based on the period of session initiation of each caller and the call duration of each receiver. As per the second mentioned problem, this scheme performs the prediction before session initiation, therefore, it's proper for real-time VoIP environment.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06d
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• pp.61-66
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• 2010

소프트웨어 보증은 소프트웨어의 신뢰성, 예측 가능한 실행으로 정의할 수 있다. 신뢰성은 악의적인 의도로 발생할 수 있는 버퍼 오버플로, 메모리 릭 등의 보안 취약점이 존재하지 않아야 한다는 것이고, 예측 가능한 실행은 소프트웨어가 개발 시 의도한대로 실행되어야 한다는 것 이다. 소프트웨어 보증을 위한 작업은 소프트웨어 개발 생명 주기의 개발과 유지보수 단계 모두에서 수행되어야 한다. 국외 기관인 NIST, SANS 등은 개발 단계에서의 보증을 위해 필요한 툴, 프로그래밍 가이드라인, 오류 식별 문서 등을 공개하고 관련 프로젝트를 지원하였다. 본 논문에서는 상대적으로 연구가 부족한 유지보수 단계에서의 소프트웨어 보증을 위해 신뢰와 평판 기반의 소프트웨어 보증 시스템을 구현하였다. 본 시스템은 사용자에게 해당 소프트웨어에 대한 다른 사용자의 평판과 다양한 의견을 제공하고, 이에 따른 신뢰도를 함께 제공한다. 이는 사용자가 안전한 소프트웨어를 선별하고 사용하는데 도움을 준다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1568-1576
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• 2015

This paper proposes a prediction reputation system for the anti-SPIT solution in real-time VoIP environment. Increased accuracy of the determination as to whether spam or not by deriving a threshold based on SPIT presence in the existing paper. The existing schemes need to get the user's feedback and/or have experienced the time delay and overload as session initiates due to real-time operation. To solve these problems, the proposed scheme predicts the reputation through the statistical analysis based on the period of session initiation of each caller and the call duration of each receiver. As per the second mentioned problem, this scheme performs the prediction before session initiation, therefore, it's proper for real-time VoIP environment.

• Journal of the HCI Society of Korea
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• v.13 no.3
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• pp.35-42
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• 2018

Social Networking Sites (hereafter SNSs) have become a part of people's daily routines. Beginning as a hobby/communicating tool for a specific group of people, SNSs have expanded in terms of the number of users, frequency of access, and depth and range of sharing. It is recently reported that the users experience SNS fatigue from being connected at all time. However, limited research has addressed this issue. The purpose of the present study was to examine the mediating role of reputational concern and expectation of others' responses on the relationship between narcissism and Facebook fatigue. A total of 329 Facebook users completed an online survey. The results showed that individuals who rated high on narcissism experienced a high level of Facebook fatigue, and that both reputational concern and expectation of others' responses partially mediated the relationship between narcissism and fatigue. Implications of the findings and suggestions for future research are discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.542-545
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• 2007

P2P 환경에서 사용자들이 직접 자료를 평가하여 악의적인 자료들을 탐지하는 평판도 방법들이 많이 연구되었다. 하지만 기존의 평판도 방법들은 자료 또는 파일 단위의 평판도를 적용하기 때문에 자료의 일부분에 대한 미세한 평가를 할 수 없으며, 특정 부분 때문에 평판도가 낮아서 자료 전체가 사용되지 못한다는 문제점을 가진다. 따라서 본 연구에서는 자료 일부분에 대한 평판도를 적용하는 새로운 평판도 방법을 제안한다. 제안하는 블록 기반의 평판도 방법은 자료의 부분에 대한 개별 평가를 하고 악의적인 블록을 배제함으로써 자료의 유익한 부분들만 사용할 수 있게 해 준다. 본 논문에서는 자료의 개별 블록 평판도와 피어의 신용도를 기반으로 하여 평판도 업데이트 방법과 개별 블록과 자료의 평가 방법을 제시한다. 또한 성능 평가를 통하여 제안하는 방법이 기존의 평판도 방법보다 자료에서 유익한 블록들만 추출하여 사용함을 보여주고 있으며, P2P 에서 발생하는 일인다역, 공모와 같은 공격에 안전함을 보이고 있다.