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Using Centrality Measures to Predict Helpfulness-Based Reputation in Trust Networks

Published:25 February 2017Publication History
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Abstract

In collaborative Web-based platforms, user reputation scores are generally computed according to two orthogonal perspectives: (a) helpfulness-based reputation (HBR) scores and (b) centrality-based reputation (CBR) scores. In HBR approaches, the most reputable users are those who post the most helpful reviews according to the opinion of the members of their community. In CBR approaches, a “who-trusts-whom” network—known as a trust network—is available and the most reputable users occupy the most central position in the trust network, according to some definition of centrality. The identification of users featuring large HBR scores is one of the most important research issue in the field of Social Networks, and it is a critical success factor of many Web-based platforms like e-marketplaces, product review Web sites, and question-and-answering systems. Unfortunately, user reviews/ratings are often sparse, and this makes the calculation of HBR scores inaccurate. In contrast, CBR scores are relatively easy to calculate provided that the topology of the trust network is known. In this article, we investigate if CBR scores are effective to predict HBR ones, and, to perform our study, we used real-life datasets extracted from CIAO and Epinions (two product review Web sites) and Wikipedia and applied five popular centrality measures—Degree Centrality, Closeness Centrality, Betweenness Centrality, PageRank and Eigenvector Centrality—to calculate CBR scores. Our analysis provides a positive answer to our research question: CBR scores allow for predicting HBR ones and Eigenvector Centrality was found to be the most important predictor. Our findings prove that we can leverage trust relationships to spot those users producing the most helpful reviews for the whole community.

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            • Published in

              cover image ACM Transactions on Internet Technology
              ACM Transactions on Internet Technology  Volume 17, Issue 1
              Special Issue on Affect and Interaction in Agent-based Systems and Social Media and Regular Paper
              February 2017
              213 pages
              ISSN:1533-5399
              EISSN:1557-6051
              DOI:10.1145/3036639
              • Editor:
              • Munindar P. Singh
              Issue’s Table of Contents

              Copyright © 2017 ACM

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 25 February 2017
              • Accepted: 1 July 2016
              • Revised: 1 April 2016
              • Received: 1 December 2015
              Published in toit Volume 17, Issue 1

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