%0 Book Section %1 PreisachPAKDD10 %A Preisach, Christine %A Marinho, Leandro Balby %A Schmidt-Thieme, Lars %B Advances in Knowledge Discovery and Data Mining %D 2010 %E Zaki, Mohammed Javeed %E Yu, Jeffrey Xu %E Ravindran, B. %E Pudi, Vikram %I Springer %K Cold-Start myown recommender tag %P 348-357 %R 10.1007/978-3-642-13657-3_38 %T Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems %U http://link.springer.com/chapter/10.1007%2F978-3-642-13657-3_38#page-1 %V 6118 %X Tag recommender systems are often used in social tagging systems, a popular family of Web 2.0 applications, to assist users in the tagging process. But in cold-start situations i.e., when new users or resources enter the system, state-of-the-art tag recommender systems perform poorly and are not always able to generate recommendations. Many user profiles contain untagged resources, which could provide valuable information especially for cold-start scenarios where tagged data is scarce. The existing methods do not explore this additional information source. In this paper we propose to use a purely graph-based semi-supervised relational approach that uses untagged posts for addressing the cold-start problem. We conduct experiments on two real-life datasets and show that our approach outperforms the state-of-the-art in many cases. %@ 978-3-642-13656-6

@incollection{PreisachPAKDD10, abstract = {Tag recommender systems are often used in social tagging systems, a popular family of Web 2.0 applications, to assist users in the tagging process. But in cold-start situations i.e., when new users or resources enter the system, state-of-the-art tag recommender systems perform poorly and are not always able to generate recommendations. Many user profiles contain untagged resources, which could provide valuable information especially for cold-start scenarios where tagged data is scarce. The existing methods do not explore this additional information source. In this paper we propose to use a purely graph-based semi-supervised relational approach that uses untagged posts for addressing the cold-start problem. We conduct experiments on two real-life datasets and show that our approach outperforms the state-of-the-art in many cases.}, added-at = {2011-01-04T14:35:42.000+0100}, author = {Preisach, Christine and Marinho, Leandro Balby and Schmidt-Thieme, Lars}, biburl = {https://www.bibsonomy.org/bibtex/259bb3eb9749b1b8444adfcbf103d5df4/lbalby}, booksubtitle = {14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I}, booktitle = {Advances in Knowledge Discovery and Data Mining}, crossref = {conf/pakdd/2010-1}, doi = {10.1007/978-3-642-13657-3_38}, editor = {Zaki, Mohammed Javeed and Yu, Jeffrey Xu and Ravindran, B. and Pudi, Vikram}, ee = {http://dx.doi.org/10.1007/978-3-642-13657-3_38}, interhash = {c808b8210844e7db08f81a118e24fffb}, intrahash = {59bb3eb9749b1b8444adfcbf103d5df4}, isbn = {978-3-642-13656-6}, keywords = {Cold-Start myown recommender tag}, pages = {348-357}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2015-08-01T03:13:28.000+0200}, title = {Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-13657-3_38#page-1}, volume = 6118, year = 2010 }