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.
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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.
|Item type:||Conference or Workshop Item (Paper)|
|Keywords:||social bookmarking, social tagging, ranking, latent dirichlet allocation, topic models, social bookmarks, Library Science. Information Science|
|Subjects:||Bibliography. Library Science. Information Resources > Library Science. Information Science|
|Department:||Faculty of Science > Computer and Information Sciences|
|Depositing user:||Pure Administrator|
|Date Deposited:||09 Jan 2013 13:14|
|Last modified:||02 Apr 2017 12:41|