Trust Prediction in Online Communities Employing Neurofuzzy Approach

Abstract

Exploiting the opportunities provided by Web 2.0 technologies has led to the appearance of social web applications, which allow users to interact and collaborate with each other, thereby sharing experience in an online environment. Establishing a trust mechanism is vital in order to identify the trustful sources of information on the social web. Trust prediction can be a useful tool for identifying the potential trust relationships among users in an online community. Thereby, reliable participants can be recommended to users; this increases social interaction among users and makes the trust network denser. The main contribution of this study is to investigate the effectiveness of soft computing and neurofuzzy techniques within the context of trust prediction. Therefore, an adaptive neurofuzzy inference system (ANFIS) as a representative of neurofuzzy techniques is compared with the widely used classification techniques including C4.5 decision tree, artificial neural network (ANN), logistic regression, and Bayesian network. All methods are applied on a portion of data obtained from the Epinions network. The results of empirical experiments indicate that ANFIS achieves the best performance in terms of area under ROC (AUC) among all other methods. Furthermore, ANFIS follows closely a C4.5 algorithm concerning F-measure. Overall, the results of experiments indicate that AFNIS can be a suitable candidate for prediction of unknown trust relationships in an online community.