Identifying and Exploiting Trustable Users with Robust Features in Online Rating Systems

Abstract

When purchasing an online product, a customer tends to be influenced strongly by its reputation, the aggregation of other customers' ratings on it. The reputation, however, is not always trustable since it can be manipulated easily by attackers who intentionally give unfair ratings to their target products. In this paper, we first address identifying trustable users who tend to give fair ratings to products in online rating systems and then propose a method of computing true reputation of a product by aggregating only those trustable users' ratings. In order to identify the trustable users, we list some candidate features that seem related significantly to the trustworthiness of users and verify the robustness of each of the features through extensive experiments. By finding and exploiting these robust features, we are able to identify trustable users and to compute true reputation effectively and efficiently based on fair ratings of those trustable users.