Ranking and Suggesting Tags in Collaborative Tagging Applications1

We consider collaborative tagging systems where users can attach tags to information objects. Such systems are widely used to add keywords meta-data to photos, videos, or web pages (social bookmarking applications). The meta-data is then used by information retrieval mechanism to provide accurate query answers. To that end, the goal of collaborative tagging systems is to quickly discover the true ranking of a tag for an information object with respect to a given ranking criteria. In this paper, we consider the popularity rank as a ranking criteria. Many collaborative tagging systems help users tagging of an object by making suggestions based on the tagging history of an information object. The problem with making tag suggestions is that they may reinforce some tags and the system fails to discover the true popularity rank of a tag for a given information object. To investigate this issue, we propose and study several algorithms for ranking and suggesting tags in collaborative tagging systems where we focus on the following design objectives: (a) learn true popularity rank, (b) make relevant suggestions, and (c) learn fast. We find that simple incremental updates of the suggestion set, which besides suggestion set size require no configuration constants, offer good performance. Performance is evaluated by analysis and numerical results for which we used a dataset of complete tagging histories of urls that we crawled from a popular social bookmarking web service over a month period.