Personalized Tag Predition Boosted by BaggTaming A Case Study of the Hatena Bookmark
Toshihiro Kamishima, Shotaro Akaho · 2008
Summary We proposed BaggTaming to boost the prediction accuracy by exploiting additional data whose class labels are less reliable. This algorithm is successfully applied to the personalized tag predicition for the data collected from the delicious. To check whether our method is generally effective, we test the data crawled from the hatena bookmark.