Social network models for enhancing reference-based search engine rankings

Korfiatis, N., Sicilia, M.-A., Hess, C., Stein, K. and Schlieder, C. (2007) Social network models for enhancing reference-based search engine rankings. In: Social Information Retrieval Systems. UNSPECIFIED, pp. 109-133. ISBN 9781599045436

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Abstract

In this chapter we discuss the integration of information retrieval information from two sources-a social network and a document reference network-for enhancing reference-based search engine rankings. In particular, current models of information retrieval are blind to the social context that surrounds information resources, thus they do not consider the trustworthiness of their authors when they present the query results to the users. Following this point we elaborate on the basic intuitions that highlight the contribution of the social context-as can be mined from social network positions for instance-into the improvement of the rankings provided in reference-based search engines. A review on ranking models in Web search engine retrieval along with social network metrics of importance such as prestige and centrality are provided as background. Then a presentation of recent research models that utilize both contexts is provided, along with a case study in the Internet-based encyclopedia Wikipedia, based on the social network metrics.

Item Type: Book Section
Faculty \ School: Faculty of Social Sciences > Norwich Business School
Related URLs:
Depositing User: Pure Connector
Date Deposited: 16 Jan 2015 16:24
Last Modified: 22 Apr 2020 10:47
URI: https://ueaeprints.uea.ac.uk/id/eprint/51376
DOI: 10.4018/978-1-59904-543-6.ch006

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