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.

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

ROSACI D;SARNE' G
2017

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.
File in questo prodotto:
File Dimensione Formato  
aroyo_2017_toit_using_editor.pdf

non disponibili

Descrizione: versione editoriale
Tipologia: Versione Editoriale (PDF)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.08 MB
Formato Adobe PDF
1.08 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
aroyo_2017_toit_using_post.pdf

accesso aperto

Descrizione: versione postprint
Tipologia: Documento in Post-print
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.03 MB
Formato Adobe PDF
1.03 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/3389
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 50
  • ???jsp.display-item.citation.isi??? 38
social impact