Social Network Models for Enhancing Reference-Based Search Engine Rankings

Social Network Models for Enhancing Reference-Based Search Engine Rankings

Nikolaos Korfiatis (Copenhagen Business School, Denmark), Miguel-Ángel Sicilia (University of Alcala, Spain), Claudia Hess (University of Bamberg, Germany), Klaus Stein (University of Bamberg, Germany) and Christoph Schlieder (University of Bamberg, Germany)
DOI: 10.4018/978-1-59904-543-6.ch006
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This chapter discusses 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 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 is provided as a 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.

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