Ranking social bookmarks using topic models

Harvey, Morgan, Ruthven, Ian and Carman, Mark J. (2010) Ranking social bookmarks using topic models. In: Proceedings of the 19th ACM international conference on Information and knowledge management. Association for Computing Machinery, New York, pp. 1401-1404. ISBN 978-1-4503-0099-5

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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 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.

Item Type:	Book Section
Subjects:	G400 Computer Science G500 Information Systems
Department:	Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User:	Morgan Harvey
Date Deposited:	27 Apr 2015 14:17
Last Modified:	10 Oct 2019 23:01
URI:	http://nrl.northumbria.ac.uk/id/eprint/22231

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