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

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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.

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Notes

  1. http://www.epinions.com.

  2. http://trust.mindswap.org/FilmTrust.

  3. http://www.wikipedia.org.

  4. http://en.wikipedia.org/wiki/Wikipedia:Size_comparisons.

  5. http://www.naymz.com.

  6. http://www.imgur.com.

  7. http://www.flickr.com.

  8. http://www.digg.com.

  9. http://www.reddit.com.

  10. http://www.stackexchange.com.

  11. http://ansonalex.com/infographics/facebook-user-statistics-2012-infographic/.

  12. http://www.techcrunch.com/2011/06/30/twitter-3200-million-tweets/.

  13. http://www.reddit.com/r/Redditresearch/comments/de4re/basic_frequency_plots_for_a_months_worth_of.

  14. http://www.wikipedia.org.

  15. http://networkx.lanl.gov.

  16. http://lucene.apache.org.

  17. http://lucene.apache.org/core/old_versioned_docs/versions/2_9_0/api/all/org/apache/lucene/search/Similarity.html.

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Acknowledgments

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

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  1. CLARITY Centre for Sensor Web Technologies, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin, Ireland

    Kevin McNally, Michael P. O’Mahony & Barry Smyth

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  1. Kevin McNally
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  2. Michael P. O’Mahony
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  3. Barry Smyth
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Correspondence to Kevin McNally.

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McNally, K., O’Mahony, M.P. & Smyth, B. A comparative study of collaboration-based reputation models for social recommender systems. User Model User-Adap Inter 24, 219–260 (2014). https://doi.org/10.1007/s11257-013-9143-6

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