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 - JOUR AU - Jäschke, Robert AU - Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd T1 - Tag Recommendations in Social Bookmarking Systems JO - AI Communications PY - 2008/ VL - 21 IS - 4 SP - 231 EP - 247 UR - http://dx.doi.org/10.3233/AIC-2008-0438 DO - 10.3233/AIC-2008-0438 KW - 2.0 KW - 2008 KW - Recommendations KW - bookmarking KW - itegpub KW - logsonomies KW - myown KW - recommendations KW - recommender KW - social KW - systems KW - tag KW - tagorapub KW - tags KW - web KW - web2.0 KW - web20 L1 - SN - N1 - N1 - AB - Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
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.
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