Commtrust: A Multi-Dimensional Trust Model for E-Commerce Applications

Abstract

E-Commerce applications use reputation-based trust models based on the feedback comments and ratings gathered. The “all better Reputation” problem for the sellers has become very huge because a buyer facing problem to choose truthful sellers. This paper proposes a new model “CommTrust” to valuate trust by mining feedback comments that uses buyer comments to calculate reputation scores using multidimensional trust model. An algorithm is proposed to mine feedback comments for dimension weights, ratings, which combine methods of topic modeling, natural language processing and opinion mining. This model has been experimenting with the dataset which includes various user level feedback comments that are obtained on various products. It also finds various multi-dimensional features and their ratings using Gibbs-sampling that generates various categories for feedback and assigns trust score for each dimension under each product level.