In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
ER - TY - CONF AU - Krause, Beate AU - Schmitz, Christoph AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems T2 - Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web PB - CY - PY - 2008/ M2 - VL - IS - SP - EP - UR - http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf M3 - KW - 2.0 KW - 2008 KW - bookmarking KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - social KW - spam KW - systems KW - tagger KW - tagorapub KW - web KW - web2.0 L1 - SN - N1 - N1 - AB - ER -