Ranking social bookmarks using topic models

Morgan Harvey, Ian Ruthven, Mark J. Carman

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Citations (Scopus)

Abstract

Ranking of resources in social tagging systems is a difficult problem due to the inherent sparsity of the data and the vocabulary problems introduced by having a completely unrestricted 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 bookmarking site. Our evaluations show that our LDA-based method significantly outperforms all of the baselines.
Original languageEnglish
Title of host publicationProceedings of the 19th ACM international conference on Information and knowledge management
Place of PublicationNew York
PublisherACM
Pages1401-1404
ISBN (Print)978-1-4503-0099-5
DOIs
Publication statusPublished - 2010
EventProceedings of the 19th {ACM} Conference on Information and Knowledge Management, {CIKM} 2010, Toronto, Ontario, Canada, October 26-30, 2010 -
Duration: 1 Jan 2010 → …

Conference

ConferenceProceedings of the 19th {ACM} Conference on Information and Knowledge Management, {CIKM} 2010, Toronto, Ontario, Canada, October 26-30, 2010
Period1/01/10 → …

Cite this