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Conference Papers Year : 2019

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 bi-partite 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.
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Dates and versions

lirmm-02112373 , version 1 (26-04-2019)

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Okba Tibermacine, Chouki Tibermacine, Mohamed Lamine Kerdoudi. Reputation Evaluation with Malicious Feedback Prevention Using a HITS-Based Model. ICWS 2019 - 26th IEEE International Conference on Web Services, Jul 2019, Milan, Italy. pp.180-187, ⟨10.1109/ICWS.2019.00039⟩. ⟨lirmm-02112373⟩
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