Acceptance of feedbacks in reputation systems: the role of online social interactions

In an online environment, the aim of reputation systems is to let parties rate each other and to help consumers in deciding whether to transact with a given party. In current reputation systems for e-commerce, users have to trust unreliable information sources and anonymous people. As a result, users are not only hesitant to trust online seller but also to reputation systems. Therefore, there is a need to improve current reputation systems by allowing users to make buying decision based on reliable source of information. This paper proposes a new approach of sharing knowledge and experience in reputation systems by utilizing social interactions. This study examines the potentials of integrating social relations information in reputation systems by proposing a model of acceptance of feedbacks in reputation systems

How Social Reputation Networks Interact with Competition in Anonymous Online Trading: An Experimental Study

Many Internet markets rely on ‘feedback systems’, essentially social networks of reputation, to facilitate trust and trustworthiness in anonymous transactions. Market competition creates incentives that arguably may enhance or curb the effectiveness of these systems. We investigate how different forms of market competition and social reputation networks interact in a series of laboratory online markets, where sellers face a moral hazard. We find that competition in strangers networks (where market encounters are one-shot) most frequently enhances trust and trustworthiness, and always increases total gains-from-trade. One reason is that information about reputation trumps pricing in the sense that traders usually do not conduct business with someone having a bad reputation not even for a substantial price discount. We also find that a reliable reputation network can largely reduce the advantage of partners networks (where a buyer and a seller can maintain repeated exchange with each other) in promoting trust and trustworthiness if the market is sufficiently competitive. We conclude that, overall, competitive online markets have more effective social reputation networks.reputation systems, e-commerce, internet markets, trust

Acceptance of Feedbacks in Reputation Systems: The Role of Online Social Interactions

In an online environment, the aim of reputation systems is to let parties rate each other and to help consumers in deciding whether to transact with a given party. In current reputation systems for e-commerce, users have to trust unreliable information sources and anonymous people. As a result, users are not only hesitant to trust online seller but also to reputation systems. Therefore, there is a need to improve current reputation systems by allowing users to make buying decision based on reliable source of information. This paper proposes a new approach of sharing knowledge and experience in reputation systems by utilizing social interactions. This study examines the potentials of integrating social relations information in reputation systems by proposing a model of acceptance of feedbacks in reputation systems

Detection and Filtering of Collaborative Malicious Users in Reputation System using Quality Repository Approach

Online reputation system is gaining popularity as it helps a user to be sure about the quality of a product/service he wants to buy. Nonetheless online reputation system is not immune from attack. Dealing with malicious ratings in reputation systems has been recognized as an important but difficult task. This problem is challenging when the number of true user's ratings is relatively small and unfair ratings plays majority in rated values. In this paper, we have proposed a new method to find malicious users in online reputation systems using Quality Repository Approach (QRA). We mainly concentrated on anomaly detection in both rating values and the malicious users. QRA is very efficient to detect malicious user ratings and aggregate true ratings. The proposed reputation system has been evaluated through simulations and it is concluded that the QRA based system significantly reduces the impact of unfair ratings and improve trust on reputation score with lower false positive as compared to other method used for the purpose.Comment: 14 pages, 5 figures, 5 tables, submitted to ICACCI 2013, Mysore, indi

The Role of Trust in the Intention to Use Feedback from Reputation Systems

Online reputation systems have evolved to increase our knowledge of sellers, products, services, and other individuals in the electronic setting of the Internet. Evidence from prior studies suggests that the feedback individuals provide through reputation systems in the form of numerical ratings, a given number of stars, and text commentary should alleviate an element of uncertainty when interacting in the online environment. However, the user of the feedback must believe that the feedback is trustworthy. To our knowledge, no studies exist which examine the role of trust with regard to the consumer’s intention to use feedback from reputation systems. As online interactions increase, mechanisms for reputation in this context continue to grow in importance. This study will endeavor to address a significant gap in current literature to examine how trust impacts the user’s intention to use feedback from online reputation systems

Reputation Systems of Online Communities Establishing a Research Agenda

Although online communities make it possible for a far greater number of participants to interact on the Web, there are challenges in creating mechanisms that reveal reputations for participants. Reputation Systems provide a proxy that establishes trust in e-commerce communities, social communities, and social news communities. There remain questions as to how reputation systems can be more widely used in online communities without damaging user confidence because participants have strong privacy expectations. This paper will review reputation systems in online communities, examine types, properties, and issues of reputation systems, survey the use of social networks and reputation systems in popular online communities, and present a research agenda to address issues of reputation systems

Expressing Trust with Temporal Frequency of User Interaction in Online Communities

Reputation systems concern soft security dynamics in diverse areas. Trust dynamics in a reputation system should be stable and adaptable at the same time to serve the purpose. Many reputation mechanisms have been proposed and tested over time. However, the main drawback of reputation management is that users need to share private information to gain trust in a system such as phone numbers, reviews, and ratings. Recently, a novel model that tries to overcome this issue was presented: the Dynamic Interaction-based Reputation Model (DIBRM). This approach to trust considers only implicit information automatically deduced from the interactions of users within an online community. In this primary research study, the Red-dit and MathOverflow online social communities have been selected for testing DIBRM. Results show how this novel approach to trust can mimic behaviors of the selected reputation systems, namely Reddit and MathOverflow, only with temporal information

Expressing Trust with Temporal Frequency of User Interaction in Online Communities

Reputation systems concern soft security dynamics in diverse areas. Trust dynamics in a reputation system should be stable and adaptable at the same time to serve the purpose. Many reputation mechanisms have been proposed and tested over time. However, the main drawback of reputation management is that users need to share private information to gain trust in a system such as phone numbers, reviews, and ratings. Recently, a novel model that tries to overcome this issue was presented: the Dynamic Interaction-based Reputation Model (DIBRM). This approach to trust considers only implicit information automatically deduced from the interactions of users within an online community. In this primary research study, the Reddit and MathOverflow online social communities have been selected for testing DIBRM. Results show how this novel approach to trust can mimic behaviors of the selected reputation systems, namely Reddit and MathOverflow, only with temporal information

Securing Online Reputation Systems Through Temporal and Trust Analysis

Securing Online Reputation Systems Through Temporal and Trust Analysi