A comparative study of collaboration-based reputation models for social recommender systems
Refereed Original Article
Today, people increasingly leverage theironlinesocial networks todiscover meaningful and relevant information, products and services. Thus, the ability to iden- tify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collabo- ration graphs ,fromwhichthereputationofusersanditemscanbederived.Weanalyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recom- mending relevant pages to users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-user trial of the system.
Digital Object Identifer (DOI):
Springer Science+Business Media Dordrecht 2013
National University of Ireland, Dublin (UCD)
Open access repository: