Tripartite hidden topic models for personalised tag suggestion

Harvey, Morgan, Baillie, Mark, Ruthven, Ian and Carman, Mark J. (2010) Tripartite hidden topic models for personalised tag suggestion. In: Advances in Information Retrieval. Lecture Notes in Computer Science, 5993 . Springer, London, pp. 432-443. ISBN 9783642122743

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Social tagging systems provide methods for users to categorise resources using their own choice of keywords (or “tags”) without being bound to a restrictive set of predefined terms. Such systems typically provide simple tag recommendations to increase the number of tags assigned to resources. In this paper we extend the latent Dirichlet allocation topic model to include user data and use the estimated probability distributions in order to provide personalised tag suggestions to users. We describe the resulting tripartite topic model in detail and show how it can be utilised to make personalised tag suggestions. Then, using data from a large-scale, real life tagging system, test our system against several baseline methods. Our experiments show a statistically significant increase in performance of our model over all key metrics, indicating that the model could be successfully used to provide further social tagging tools such as resource suggestion and collaborative filtering.

Item Type: Book Section
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
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Depositing User: Morgan Harvey
Date Deposited: 27 Apr 2015 13:39
Last Modified: 12 Oct 2019 22:29

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