Personalization in Folksonomies Based on Tag Clustering

Contents

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-defined navigational or conceptual hierarchies. However, the flexibility 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 difficult. 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. Specifically, we examine unsupervised clustering methods for extracting commonalities between tags, and use the discovered clusters as intermediaries between a user's profile 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.