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Dynamics of a Collaborative Rating System

  • Conference paper
Advances in Web Mining and Web Usage Analysis (SNAKDD 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5439))

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Abstract

The rise of social media sites, such as blogs, wikis, Digg and Flickr among others, underscores a transformation of the Web to a participatory medium in which users are actively creating, evaluating and distributing information. The social news aggregator Digg allows users to submit links to and vote on news stories. Like other social media sites, Digg also allows users to designate others as “friends” and easily track friends’ activities: what new stories they submitted, commented on or liked. Each day Digg selects a handful of stories to feature on its front page. Rather than rely on the opinion of a few editors, Digg aggregates opinions of thousands of its users to decide which stories to promote to the front page. We construct two mathematical models of collaborative decision-making on Digg. First, we study how collective rating of news stories emerges from the decisions made by many users. The model takes into account the effect that decisions made by a user’s friends have on the user. We also study how user’s influence, as measured by her rank, changes in time as she submits new stories and is befriended by other Digg users. Solutions of both models reproduce the observed dynamics of voting and user rank on Digg.

The Digg model that enables users to collectively rate news stories can be generalized to the collaborative evaluation of document (or information) quality. Mathematical analysis can be used as a tool to explore different collaborative decision-making algorithms to select the most effective one before the algorithm is ever implemented in a real system.

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Author information

Authors and Affiliations

  1. USC Information Sciences Institute, Marina del Rey, California, 90292, USA

    Kristina Lerman

Authors
  1. Kristina Lerman
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Editor information

Editors and Affiliations

  1. One Microsoft Way, WA 98052, Redmond, USA

    Haizheng Zhang

  2. Faculty of Computer Science, Otto von Guericke University Magdeburg, 39016, Magdeburg, Germany

    Myra Spiliopoulou

  3. School of Computing Science, Telecommunications Information Systems, DePaul University, IL 60604, Chicago, USA

    Bamshad Mobasher

  4. College of Information Science and Technology, Pennsylvania State University, PA 16802, University Park, USA

    C. Lee Giles

  5. Department of Computer Science, University of Massachusetts, MA 01003, Amherst, USA

    Andrew McCallum

  6. Computer Engineering and Computer Science Department, University of Louisville, KY 40292, Louisville, USA

    Olfa Nasraoui

  7. Department of Computer Science and Engineering, University of Minnesota, MN 55455, Minneapolis, USA

    Jaideep Srivastava

  8. College of Information Sciences and Technology, The Pennsylvania State University, PA 16802, Unversity Park, USA

    John Yen

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© 2009 Springer-Verlag Berlin Heidelberg

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Lerman, K. (2009). Dynamics of a Collaborative Rating System. In: Zhang, H., et al. Advances in Web Mining and Web Usage Analysis. SNAKDD 2007. Lecture Notes in Computer Science(), vol 5439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00528-2_5

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  • DOI: https://doi.org/10.1007/978-3-642-00528-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00527-5

  • Online ISBN: 978-3-642-00528-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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