@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
}

@incollection{citeulike:6386729,
  abstract = {Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user's important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.},
  address = {Berlin, Heidelberg},
  author = {Liang, Huizhi and Xu, Yue and Li, Yuefeng and Nayak, Richi},
  booktitle = {Rough Sets and Knowledge Technology },
  chapter = 84,
  citeulike-article-id = {6386729},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84},
  citeulike-linkout-1 = {http://www.springerlink.com/content/f66k11352q386379},
  doi = {10.1007/978-3-642-02962-2\_84},
  editor = {Wen, Peng and Li, Yuefeng and Polkowski, Lech and Yao, Yiyu and Tsumoto, Shusaku and Wang, Guoyin},
  interhash = {80e8a1d0263296925609dbd5b72b7d48},
  intrahash = {bf98d7c1fee5f2f188f529701e70199f},
  isbn = {978-3-642-02961-5},
  pages = {666--673},
  posted-at = {2009-12-15 15:06:20},
  priority = {2},
  publisher = {Springer Berlin Heidelberg},
  title = {Tag Based Collaborative Filtering for Recommender Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84},
  volume = 5589,
  year = 2009
}

@inproceedings{conf/www/SarwarKKR01,
  author = {Sarwar, Badrul M. and Karypis, George and Konstan, Joseph A. and Riedl, John},
  booktitle = {WWW},
  ee = {http://doi.acm.org/10.1145/371920.372071},
  interhash = {043d1aaba0f0b8c01d84edd517abedaf},
  intrahash = {f349b429624935212ebeed613b89794f},
  pages = {285-295},
  title = {Item-based collaborative filtering recommendation algorithms.},
  url = {http://www10.org/cdrom/papers/pdf/p519.pdf},
  year = 2001
}

@inproceedings{jaeschke07tagKdml,
  author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
  booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)},
  editor = {Hinneburg, Alexander},
  interhash = {7e212e3bac146d406035adebff248371},
  intrahash = {bfc43dfe59f9c0935ac3364b12e6d795},
  isbn = {978-3-86010-907-6},
  month = sep,
  pages = {13-20},
  publisher = {Martin-Luther-Universität Halle-Wittenberg},
  title = {Tag Recommendations in Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf},
  vgwort = {20},
  year = 2007
}

@inproceedings{breese98empirical,
  author = {Breese, John S. and Heckerman, David and Kadie, Carl},
  booktitle = {Proceedings of the 14$^{th}$ Conference on Uncertainty in Artificial Intelligence},
  interhash = {593f72dfa20e4b7b5b16205479989020},
  intrahash = {82cd7b6c312f4181b1d05adb10c1d56a},
  pages = {43-52},
  title = {Empirical Analysis of Predictive Algorithms for Collaborative Filtering},
  year = 1998
}

@inproceedings{Byde2007,
  abstract = {This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.},
  author = {Byde, Andrew and Wan, Hui and Cayzer, Steve},
  booktitle = {Proceedings of the International Conference on Weblogs and Social Media},
  interhash = {38aaca7e5b9c508a5901f4109dabaa69},
  intrahash = {157846898c1c2a65c265a913ebac115a},
  month = {March},
  priority = {5},
  title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics},
  url = {http://www.icwsm.org/papers/paper47.html},
  year = 2007
}

@article{963774,
  address = {New York, NY, USA},
  author = {Hofmann, Thomas},
  doi = {http://doi.acm.org/10.1145/963770.963774},
  interhash = {ffd4c7560d25f5c0b6f92dbba0bbfc79},
  intrahash = {a887c9d3b1d49ae260a7b3cd1118d36a},
  issn = {1046-8188},
  journal = {ACM Trans. Inf. Syst.},
  number = 1,
  pages = {89--115},
  publisher = {ACM Press},
  title = {Latent semantic models for collaborative filtering},
  volume = 22,
  year = 2004
}

@article{245123,
  address = {New York, NY, USA},
  author = {Kautz, Henry and Selman, Bart and Shah, Mehul},
  doi = {http://doi.acm.org/10.1145/245108.245123},
  interhash = {6995678b936b33eef9ea1396e53a1fc7},
  intrahash = {ba3606b3aa6c4cf94784db451b28cd68},
  issn = {0001-0782},
  journal = {Commun. ACM},
  number = 3,
  pages = {63--65},
  publisher = {ACM Press},
  title = {Referral Web: combining social networks and collaborative filtering},
  volume = 40,
  year = 1997
}

@article{citeulike:171426,
  author = {Adomavicius, G. and Tuzhilin, A.},
  citeulike-article-id = {171426},
  interhash = {42f7653127a823354d000ea95cf804be},
  intrahash = {55294392edb717922798725dd8be80b3},
  journal = {Knowledge and Data Engineering, IEEE Transactions on},
  keywords = {collaborative collaborative-filtering filtering mining personalization recommender recommender-systems systems},
  number = 6,
  pages = {734--749},
  priority = {2},
  title = {Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1423975},
  volume = 17,
  year = 2005
}

@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
}