Fuzzy Trust Aggregation and Personalized Trust Inference in Virtual Social Networks

Abstract

Virtual marketplaces on the Web provide people with great facilities to buy and sell goods similar to conventional markets. In traditional business, reputation is subjectively built for known persons and companies as the deals are made in the course of time. As it is important to do business with trustful individuals and companies, there is a need to survive the reputation concept in virtual markets. Auction sites generally employ reputation systems based on feedbacks that provide a global view to a cyber dealer. In contrast to global trust, people usually infer their personal trust about someone whose reputation is completely or partially unknown by asking their trusted friends.Personal reputation is what makes a person trusted for some people and untrusted for others. There should be a facility for users in a virtual market to specify how much they trust a friend and also a mechanism that infers the trust of a user to another user who is not directly a friend of her. There are two main issues that should be addressed in trust inference. First, the trust modeling and aggregation problem needs to be challenged. Second, algorithms should be introduced to find and select the best paths among the existing trust paths from a source to a sink. First, as trust to a person can be stated more naturally using linguistic expressions, this work suggests employing linguistic terms for trust specification. To this end, corresponding fuzzy sets are defined for trust linguistic terms and a fuzzy trust aggregation method is also proposed. Comparing the fuzzy aggregation method to the existing aggregation methods shows superiority of fuzzy approach especially at aggregating contradictory information. Second, this paper proposes an incremental trust inference algorithm. The results show improvement in preciseness of inference for the proposed inference algorithm over the existing and recently proposed algorithm named TidalTrust.

Determining credibility from social network structure

“…Our work differs from other investigations into trust and trust propagation (e.g., [8], [9], [10]), as we explore individuals' judgments of credibility as they make their determination through the observation of ego-centric network properties in isolation from other traditional indicators. Here, credibility is not evaluated over established friend networks (which might carry a host of additional information) or those that have been built and maintained over time (where dynamic information might come into play).…”

“…In many studies, trust is the relying party's subjective belief in an entity to serve a certain function, such as information provision [8]. Reputation, in this context, implies permanence of a relationship and is therefore not immediately relevant to our study that concentrates on immediately perceivable social network properties.…”

A Web‐based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques

“…Future studies will focus on further improvement of the recommendations quality of the proposed HFSR approach. As the trust and reputation systems represent a significant trend in decision support systems and commercial online applications (Lesani and Montazeri 2009), the first future task is to consider incorporating a reputation mechanism using social network techniques into our Smart BizSeeker system. Thus, by using the aggregated ratings about a given business entity, we can build the “web of trust” for that business entity and derive its trust or reputation score.…”

Fuzzy Computational Models of Trust and Distrust for Enhanced Recommendations

“…Another concern is related to trust‐enhanced RSs is trust modeling. Sometimes crisp modeling of trust and distrust is not enough for inferring accurate information especially in contradictory situations, averaging the trust–distrust values and furthermore, people naturally use linguistic expressions rather than numeric values to describe their trust . But in existing trust networks, users specify their trusts and distrusts to other persons in numeric values.…”

“…Sometimes crisp modeling of trust and distrust is not enough for inferring accurate information especially in contradictory situations, averaging the trustdistrust values and furthermore, people naturally use linguistic expressions rather than numeric values to describe their trust. 14 But in existing trust networks, users specify their trusts and distrusts to other persons in numeric values. For example, in seven scale ratings, for optimistic users, a rating of 7 may mean highly trusted but for pessimistic users it may mean somewhat high trusted.…”