Improved annotation of the blogosphere via autotagging and hierarchical clustering
Proceedings of the 15th international conference on World Wide Web
AutoTag: a collaborative approach to automated tag assignment for weblog posts
Proceedings of the 15th international conference on World Wide Web
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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We present iTag, a personalized tag recommendation system for blogs. iTag improves on current tag recommendation systems in two ways. First, iTag has much higher precision and recall than previously proposed tagging algorithms. For example, iTag achieved over 60% precision and recall on a set of 1000 blog posts selected at random from a WordPress RSS feed in April 2009, whereas the previously developed TagAssist achieved less than 10% precision and recall on our data. Second, iTag performs just as well when trained on a single user's blog as when trained on a large corpus of blogs. Thus, iTag can be deployed as a global, non-personalized tag recommendation system, or as a personalized tag recommender. Our experiments and survey of tagging behavior suggest that bloggers use tags idiosyncratically, so personalized tagging is an important option.