@inproceedings{melville2002contentboosted,
  abstract = {Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, <i>Content-Boosted Collaborative Filtering</i>, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.},
  acmid = {777124},
  address = {Menlo Park, CA, USA},
  author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass},
  booktitle = {Eighteenth National Conference on Artificial Intelligence},
  interhash = {985028099c1a29f116ad7434005895ac},
  intrahash = {a4917f0299f48e403966a8003ebd50be},
  isbn = {0-262-51129-0},
  location = {Edmonton, Alberta, Canada},
  numpages = {6},
  pages = {187--192},
  publisher = {American Association for Artificial Intelligence},
  title = {Content-boosted Collaborative Filtering for Improved Recommendations},
  url = {http://dl.acm.org/citation.cfm?id=777092.777124},
  year = 2002
}

@inproceedings{illig2009comparison,
  abstract = {Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. },
  address = {Berlin/Heidelberg},
  author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Knowledge Processing and Data Analysis},
  doi = {10.1007/978-3-642-22140-8_9},
  editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs},
  interhash = {cd3420c0f73761453320dc528b3d1e14},
  intrahash = {f9d6e06ab0f2fdcebb77afa97d72e40a},
  isbn = {978-3-642-22139-2},
  pages = {136--149},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9},
  vgwort = {23},
  volume = 6581,
  year = 2011
}

@inproceedings{schmitz2006content,
  address = {Budva, Montenegro},
  author = {Schmitz, Christoph and Hotho, Andreas and J\"aschke, Robert and Stumme, Gerd},
  booktitle = {Proceedings of the 3rd European Semantic Web Conference},
  interhash = {940fa3c671c771cc9a644b3ecfef43cd},
  intrahash = {9a06428ec3bd72e3ea6c7a8f08e2bb85},
  isbn = {3-540-34544-2},
  month = {June},
  pages = {530-544},
  publisher = {Springer},
  series = {LNCS},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdf},
  vgwort = {27},
  volume = 4011,
  year = 2006
}

@inproceedings{lang95newsweeder,
  author = {Lang, Ken},
  booktitle = {Proceedings of the 12th International Conference on Machine Learning},
  interhash = {e64ed50bf2d9ceb44e38ec59c0947207},
  intrahash = {b738abb5a0f2cae47e8f0633460c69a3},
  pages = {331--339},
  publisher = {Morgan Kaufmann publishers Inc.: San Mateo, CA, USA},
  title = {News{W}eeder: learning to filter netnews},
  url = {http://citeseer.ist.psu.edu/lang95newsweeder.html},
  year = 1995
}

@inproceedings{Melvilleetal,
  author = {Melville, P. and Mooney, R.J. and Nagarajan, R.},
  booktitle = {Proceedings of the ACM SIGIR Workshop on Recommender Systems},
  interhash = {24829f5f483b599d2fb8b225a24b7d1b},
  intrahash = {beebf3ef144ec0d59135a1f472f2f692},
  location = {New Orleans, LA},
  month = Sep,
  title = {Content-boosted collaborative filtering},
  year = 2001
}