Tripartite hidden topic models for personalised tag suggestion

Morgan Harvey, Mark Baillie, Ian Ruthven, Mark J. Carman

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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 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.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval
EditorsCathal Gurrin, Yulan He, Gabriella Kazai, Udo Kruschwitz, Suzanne Little, Thomas Roelleke, Stefan Rüger, Keith van Rijsbergen
Place of PublicationLondon
ISBN (Print)9783642122743
Publication statusPublished - 2010
Event32nd European Conference on Information Retrieval - Milton Keynes
Duration: 1 Jan 2010 → …

Publication series

NameLecture Notes in Computer Science


Conference32nd European Conference on Information Retrieval
Period1/01/10 → …
Internet address


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