Personalised Tag Recommendation

Personalised tag recommenders are becoming increasingly important since they are useful for many document management applications including social bookmarking websites. This paper presents a novel approach to the problem of suggesting personalised tags for a new document to the user. Document similarity in combination with a user similarity measure is used to recommend personalised tags. In case the existing tags in the system do not seem suitable for the userdocument pair, new tags are generated from the content of the new document as well as existing documents using document clustering. A first evaluation of the system was carried out on a dataset from the social bookmaking website, Bibsonomy. The results of this initial test indicate that adding personalisation to an unsupervised system through our user similarity measure gives an increase in the precision score of the system.