One-Class SVMs for Personalized Tag-based Resource Classification in Social Bookmarking Systems

Social tagging systems allow users to easily create, organize and share collections of Web resources in a collaborative fashion. Videos, pictures, research papers and Web pages are shared and annotated in sites such as Del.icio.us, CiteULike or Flickr, among others. The rising popularity of these systems leads to a constant increase in the number of users actively publishing and annotating resources and, consequently, an exponential growth in the amount of data contained in their folksonomies, the underlying data structure of tagging systems. In turn, the user task of discovering interesting resources becomes more and more difficult and time-consuming. In this paper the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class Support Vector Machine (SVM) classification is evaluated as a means to identify relevant information for users based exclusively on positive examples of their information preferences. It is assumed that users express their interest on resources belonging to a folksonomy by assigning tags to them, whereas there is not an straightforward method to collect uninterestingness judgments. Filtering interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities of Web communities. In this paper, the results achieved with tag-based classification are compared with those obtained using more traditional information sources such as the full-text of Web pages. Experimental evaluation showed that tag-based classifiers outperformed those learned using the text of documents as well as other content-related sources. Moreover, tag-based classification becomes essential for folksonomies in which no additional content is available because of the nature of resources being stored (e.g. tagging of photos or videos).