@inproceedings{gemmell2009improving,
  abstract = {Collaborative tagging applications allow users to annotate online  resources. The result is a complex tapestry of interrelated users, resources  and tags often called a folksonomy. Folksonomies present  an attractive target for data mining applications such as tag recommenders.  A challenge of tag recommendation remains the adaptation  of traditional recommendation techniques originally designed  to work with two dimensional data. To date the most successful  recommenders have been graph based approaches which explicitly  connects all three components of the folksonomy.  In this paper we speculate that graph based tag recommendation  can be improved by coupling it with item-based collaborative  filtering. We motive this hypothesis with a discussion of informational  channels in folksonomies and provide a theoretical explanation  of the additive potential for item-based collaborative filtering.  We then provided experimental results on hybrid tag recommenders  built from graph models and other techniques based on popularity,  user-based collaborative filtering and item-based collaborative filtering.  We demonstrate that a hybrid recommender built from a graph  based model and item-based collaborative filtering outperforms its  constituent recommenders.  furthermore the inability of the other  recommenders to improve upon the graph-based approach suggests  that they offer information already included in the graph based  model. These results confirm our conjecture. We provide extensive  evaluation of the hybrids using data collected from three real  world collaborative tagging applications.},
  author = {Gemmell, Jonathan and Schimoler, Thomas R. and Christiansen, Laura and Mobasher, Bamshad},
  booktitle = {ACM RecSys'09 Workshop on Recommender Systems and the Social Web},
  editor = {Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Anand, Sarabjot Singh and Dugan, Casey and Mobasher, Bamshad and Kobsa, Alfred},
  interhash = {0900f921d87c5ee19a4ed2c70e5a71df},
  intrahash = {6b1ff3b7b691b84288fb7122968134c4},
  issn = {1613-0073},
  month = oct,
  pages = {17--24},
  series = {CEUR-WS.org},
  title = {Improving Folkrank With Item-Based Collaborative Filtering},
  url = {http://ceur-ws.org/Vol-532/paper3.pdf},
  volume = 532,
  year = 2009
}