Ranking social bookmarks using topic models

M. Harvey, I. Ruthven, M. Carman

Research output: Contribution to conferencePaper

6 Citations (Scopus)
91 Downloads (Pure)


Ranking of resources in social tagging systems is a difficult problem due to the inherent sparsity of the data and the vo- cabulary problems introduced by having a completely unre- stricted lexicon. In this paper we propose to use hidden topic models as a principled way of reducing the dimensionality of this data to provide more accurate resource rankings with higher recall. We first describe Latent Dirichlet Allocation (LDA) and then show how it can be used to rank resources in a social bookmarking system. We test the LDA tagging model and compare it with 3 non-topic model baselines on a large data sample obtained from the Delicious social book- marking site. Our evaluations show that our LDA-based method significantly outperforms all of the baselines.
Original languageEnglish
Number of pages4
Publication statusPublished - 1 Oct 2010
Event19th ACM international conference on Information and knowledge management - Toronto, Canada
Duration: 26 Oct 201030 Oct 2010


Conference19th ACM international conference on Information and knowledge management


  • social bookmarking
  • social tagging
  • ranking
  • latent dirichlet allocation
  • topic models
  • social bookmarks


Dive into the research topics of 'Ranking social bookmarks using topic models'. Together they form a unique fingerprint.

Cite this