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ECIR
2010
Springer

Tripartite Hidden Topic Models for Personalised Tag Suggestion

11 years 3 months ago
Tripartite Hidden Topic Models for Personalised Tag Suggestion
Abstract. 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 f...
Morgan Harvey, Mark Baillie, Ian Ruthven, Mark Jam
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2010
Where ECIR
Authors Morgan Harvey, Mark Baillie, Ian Ruthven, Mark James Carman
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