Ranking social bookmarks using topic models

Harvey, M. and Ruthven, I. and Carman, M. (2010) Ranking social bookmarks using topic models. In: 19th ACM international conference on Information and knowledge management, 2010-10-26 - 2010-10-30. (https://doi.org/10.1145/1871437.1871632)

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Abstract

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.

ORCID iDs

Harvey, M., Ruthven, I. ORCID logoORCID: https://orcid.org/0000-0001-6669-5376 and Carman, M.;
CORE (COnnecting REpositories)