Recommending Tags for New Resources in Social Bookmarking Systems

Social bookmarking system is a web-based resource sharing system that allows users to upload, share and organize their resources i.e. bookmarks and publications. The system has shifted the paradigm of bookmarking from an individual activity limited to desktop to a collective activity on the web. It also facilitates user to annotate his resource with free form tags that leads to large communities of users to collaboratively create accessible repositories of web resources. Tagging process has its own challenges like ambiguity, redundancy or misspelled tags and sometimes user tends to avoid it as he has to describe tag at his own. The resultant tag space is noisy or very sparse and dilutes the purpose of tagging. The effective solution is Tag Recommendation System that automatically suggests appropriate set of tags to user while annotating resource. In this paper, we propose a framework that does not depend on tagging history of the resource or user and thereby capable of suggesting tags to the resources which are being submitted to the system first time. We model tag recommendation task as multi-label text classification problem and use Naive Bayes classifier as the base learner of the multilabel classifier. We experiment with Boolean, bag-of-words and term frequency-inverse document frequency (TFIDF) representation of the resources and fit appropriate distribution to the data based on the representation used. Impact of feature selection on the effectiveness of the tag recommendation is also studied. Effectiveness of the proposed framework is evaluated through precision, recall and f-measure metrics.