TACO: a novel method for trust rating subjectivity elimination based on Trust Attitudes COmparison

Abstract

Trust ratings shared by users in electronic commerce environments are subjective as trust evaluation depends on evaluators' personal disposition to trust. As such, aggregation of shared trust ratings to compute a user's reputation may be questionable without proper consideration of rating subjectivity. Although the problem of subjectivity in trust opinions has already been recognized, it has not been adequately resolved so far. In this paper, we address the problem of proper trust rating analysis and aggregation, which includes elimination of subjectivity. We propose a novel method based on Trust Attitudes COmparison (TACO method), which derives adjusted reputations compliant with the behavioral patterns of the evaluators and eliminates the subjectivity from the trust ratings. With the TACO method, all participants have comparable opportunities to choose trustworthy transaction partners, regardless of their trust dispositions. The TACO method finds the users with similar trust attitudes, taking advantage of nonparametric statistical methods. After that, it computes the personalized reputation scores of other users with the aggregation of trust values shared by users with similar trust attitudes. The method derives the characteristics of participants' trust dispositions implicitly from their past ratings and does not request them to disclose any part of their trust evaluation process, such as motivating criteria for trust assessments, underlying beliefs, or criteria preferences. We have evaluated the performance of our method with extensive simulations with varying numbers of users, different numbers of available trust ratings, and with different distributions of users' personalities. The results showed significant improvements using our TACO method with an average improvement of 50.0 % over the Abdul-Rahman and 72.9 % over the Hasan method.