Automatic Web Tagging and Person Tagging Using Language Models

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Abstract

Social tagging systems, such as Delicious, My Web 2.0, Flickr, YouTube, have been very successful and attracted hundreds of million users. User provided tags of an object/page can be used to help the user re-find the object through search or share the customized object with other people.

Instead of waiting for a user to find and input the appropriate words to tag an object, we propose to automatically recommend tags for user to choose from, a process that requires much less cognitive effort than traditional tagging. In particular, we formalize the tag suggestion problem as a ranking problem and propose a new probabilistic language model to rank meaningful tags, including words or phrases, for bookmarks. Besides, we adapt the probabilistic language model to tag users. The user tags can be viewed as recommended queries for the user to search documents. They can also be used as meta data about the users, which could be beneficial for people search or person recommendation. The effectiveness of the proposed techniques are demonstrated on data collected from del.icio.us.