%0 Conference Paper %1 gem08personalization %A Gemmell, J. %A Shepitsen, A. %A Mobasher, M. %A Burke, R. %B Proceedings of the 6th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems %D 2008 %K clustering recommendation tagging %T Personalization in Folksonomies Based on Tag Clustering %X Collaborative tagging systems, sometimes referred to as "folksonomies", enable Internet users to annotate or search for resources using custom labels instead of being restricted by pre-de
ned navigational or conceptual hierarchies. However, the exibility of tagging brings with it certain costs. Because users are free to apply any tag to any resource, tagging systems contain large numbers of redundant, ambiguous, and idiosyncratic tags which can render resource discovery dif
cult. Data mining techniques such as clustering can be used to ameliorate this problem by reducing noise in the data and identifying trends. In particular, discovered patterns can be used to tailor the system's output to a user based on the user's tagging behavior. In this paper, we propose a method to personalize a user's experience within a folksonomy using clustering. A personalized view can overcome ambiguity and idiosyncratic tag assignment, presenting users with tags and resources that correspond more closely to their intent. Speci
cally, we examine unsupervised clustering methods for extracting commonalities between tags, and use the discovered clusters as intermediaries between a user's pro
le and resources in order to tailor the results of search to the user's interests. We validate this approach through extensive evaluation of proposed personalization algorithm and the underlying clustering techniques using data from a real collaborative tagging Web site.

@inproceedings{gem08personalization, abstract = {Collaborative tagging systems, sometimes referred to as "folksonomies", enable Internet users to annotate or search for resources using custom labels instead of being restricted by pre-de
ned navigational or conceptual hierarchies. However, the exibility of tagging brings with it certain costs. Because users are free to apply any tag to any resource, tagging systems contain large numbers of redundant, ambiguous, and idiosyncratic tags which can render resource discovery dif
cult. Data mining techniques such as clustering can be used to ameliorate this problem by reducing noise in the data and identifying trends. In particular, discovered patterns can be used to tailor the system's output to a user based on the user's tagging behavior. In this paper, we propose a method to personalize a user's experience within a folksonomy using clustering. A personalized view can overcome ambiguity and idiosyncratic tag assignment, presenting users with tags and resources that correspond more closely to their intent. Speci
cally, we examine unsupervised clustering methods for extracting commonalities between tags, and use the discovered clusters as intermediaries between a user's pro
le and resources in order to tailor the results of search to the user's interests. We validate this approach through extensive evaluation of proposed personalization algorithm and the underlying clustering techniques using data from a real collaborative tagging Web site.}, added-at = {2010-09-10T17:42:35.000+0200}, author = {Gemmell, J. and Shepitsen, A. and Mobasher, M. and Burke, R.}, biburl = {https://www.bibsonomy.org/bibtex/2a7dfaeb3b087ce35618f01d4a54651e5/glanebridge}, booktitle = {Proceedings of the 6th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems}, interhash = {bc69ff7a13047e5a0dc4cc18d9bb9866}, intrahash = {a7dfaeb3b087ce35618f01d4a54651e5}, keywords = {clustering recommendation tagging}, location = {Chicago, Illinois, USA}, month = {July}, timestamp = {2010-09-10T17:42:35.000+0200}, title = {Personalization in Folksonomies Based on Tag Clustering}, year = 2008 }