A reliable reputation computation framework for online items in E-commerce

© 2019 Elsevier Ltd Most of online trading platforms allow consumers to give personal ratings to online items. By computing the weighted mean of the ratings, the reputation values of online items can be derived to assist consumers to make purchasing decisions. However, it is never a simple task to derive a reliable reputation value of any given item and existing works fail to achieve this. Thus, in this paper, we propose a reliable reputation computation framework for online items which can be adopted by online trading platforms or run by a third party to provide reputation computation as a service. At first, a fine-grained two-phase detection method is proposed to detect malicious ratings. After filtering out the ratings detected as malicious, the weights of the remaining ratings are determined by computing the degrees to which the users giving these ratings are interested in a target item. Extensive experiments verify that the proposed reliable reputation computation framework is effective to detect different kinds of malicious ratings and determine the interest degrees of users.