Tripartite hidden topic models for personalised tag suggestion

M. Harvey, M. Baillie, M. Carman, I. Ruthven

Research output: Contribution to conferenceOther

18 Citations (Scopus)


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 tri-partite 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.
Original languageEnglish
Publication statusPublished - 28 Mar 2010
Event32nd European Conference on Information Retrieval - Open University, Milton Keynes, United Kingdom
Duration: 28 Mar 201031 Mar 2010


Conference32nd European Conference on Information Retrieval
Abbreviated titleECIR 2010
Country/TerritoryUnited Kingdom
CityMilton Keynes
Internet address


  • personalised tags
  • hidden topics


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