Reputation Evaluation with Malicious Feedback Prevention Using a HITS-Based Model

Abstract

The reputation of web services is calculated by aggregating user feedback ratings. Though reputation is a subjective metric, it can be considered as a good indicator about services Quality of Experience, and henceforth, it can be used for recommending services in an open ecosystem. In this work, we propose a three-phase process for evaluating web service reputation by aggregating user feedback ratings. The relationship between users and services is modeled as a bipartite graph where an adapted HITS (Hypertext Induced Topic Search) algorithm is employed to distinguish between honest and malicious users in Phase I. Then, this model is used to evaluate, in Phase III, the reputation of web services from user ratings after punishing malicious users in Phase II. An experiment on a dataset of real Web services was conducted to validate the effectiveness of the proposed model in evaluating Web service reputation.