Automatic Tag Suggestion Based on Resource Contents
Although social tagging systems are becoming increasingly popular, tagging is still usually a manual process. When publishing on a social tagging system, the user is asked for the tags he wishes to assign to the resource being made available. In this paper, we present an automatic tag suggester, Tess. Our system makes recommendations based only on the textual contents of the resource and is independent of existing tags, thus allowing the emergence of novel tags. The system was evaluated by a group of users and statistical measures were applied to infer its performance. Results show that the system is not only able to suggest many useful tags, but also to discover new and relevant tags, not suggested by any of the human users.
KeywordsTextual Content Term Weight Inverse Document Frequency Similar Document Query Vector
Unable to display preview. Download preview PDF.
- 2.Waters, D.: Readers are digging the news online. BBC News (June 2006), http://news.bbc.co.uk/2/hi/technology/5128386.stm
- 3.Mishne, G.: Autotag: a collaborative approach to automated tag assignment for weblog posts. In: Proceedings of the 15th international conference on World Wide Web, pp. 953–954 (2006)Google Scholar
- 4.Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 506–514 (2007)Google Scholar
- 5.Chirita, P.A., Costache, S., Nejdl, W., Handschuh, S.: P-TAG: large scale automatic generation of personalized annotation tags for the Web. In: Proceedings of the 16th international conference on World Wide Web, pp. 845–854 (2007)Google Scholar
- 7.Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceedings of the 14th International Conference on Machine Learning, pp. 412–420 (1997)Google Scholar