1. Social Ranking:
Uncovering Relevant
Content Using Tag-based
Recommender Systems
Valentina Zanardi
Licia Capra
Dept. of Computer Science
University College London

2. Outline
• Problem definition
• Dataset analysis
• Social Ranking Query Model
• Evaluation of Social Ranking
• Related work
• Conclusion and future work
2

3. Problem definition
• Content overload
• Personalization of content: Social tagging
behaviour*
* S. Golder and B.A.Huberman “Usage patterns of collaborative tagging systems” Journal of Information Science,
32(2):198-208, 2006 3

4. Dataset Analysis
CiteULike social bookmarking website:
• allows the sharing of scientific references amongst
researchers
• freely tagged content
100.000 papers
Prune
55.000 tags
Detailed results analyzed in V. Zanardi and L. Capra. quot;Social Ranking: Finding
Relevant Content in Web 2.0quot; ECAI, 18th European Conference on Artificial
Intelligence, Patras, Greece. July 2008. 4

5. Insight from CiteULike analysis
• Each user only bookmark a tiny
portion of the whole paper set.
• The vocabulary spoken by each
user is a tiny proportion of the
emerging folksonomy.
Standard information retrieval system yield poor performance :
for papers tagged only by a small subset of users
Accuracy
for tags used only by a small subset of users, due to the empty
Coverage
overlap between tags
5

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