The emergence of Web 2.0 and the consequent success of social network websites such as del.icio.us and Flickr introduce us to a new concept called social bookmarking, or tagging in short. Tagging can be seen as the action of connecting a relevant user-defined keyword to a document, image or video, which helps user to better organize and share their collections of interesting stuff. With the rapid growth of Web 2.0, tagged data is becoming more and more abundant on the social network websites. %Web sites which populate tagging services %offer a good way for Internet users to share their knowledge. An interesting problem is how to automate the process of making tag recommendations to users when a new resource becomes available.
In this paper, we address the issue of tag recommendation from a machine learning perspective of view. From our empirical observation of two large-scale data sets, we first argue that the user-centered approach for tag recommendation is not very effective in practice. Consequently, we propose two novel document-centered approaches that are capable of making effective and efficient tag recommendations in real scenarios. The first graph-based method represents the tagged data into two bipartite graphs of (document, tag) and (document, word), then finds document topics by leveraging graph partitioning algorithms. The second prototype-based method aims at finding the most representative documents within the data collections and advocates a sparse multi-class Gaussian process classifier for efficient document classification. For both methods, tags are ranked within each topic cluster/class by a novel ranking method. Recommendations are performed by first classifying a new document into one or more topic clusters/classes, and then selecting the most relevant tags from those clusters/classes as machine-recommended tags.