User Modeling and User-Adapted Interaction

, Volume 24, Issue 3, pp 219–260 | Cite as

A comparative study of collaboration-based reputation models for social recommender systems

  • Kevin McNally
  • Michael P. O’Mahony
  • Barry Smyth
Original Paper

Abstract

Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify 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 collaboration graphs, from which the reputation of users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recommending 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.

Keywords

Reputation Social recommender systems Collaboration graphs 

Notes

Acknowledgments

This work is supported by Science Foundation Ireland under grant 07/CE/I1147.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Kevin McNally
    • 1
  • Michael P. O’Mahony
    • 1
  • Barry Smyth
    • 1
  1. 1.CLARITY Centre for Sensor Web Technologies, School of Computer Science and InformaticsUniversity College DublinDublinIreland

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