@inproceedings{tso2008tag,
  abstract = {Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.},
  address = {New York, NY, USA},
  author = {Tso-Sutter, Karen H. L. and Marinho, Leandro Balby and Schmidt-Thieme, Lars},
  booktitle = {SAC '08: Proceedings of the 2008 ACM symposium on Applied computing},
  doi = {http://doi.acm.org/10.1145/1363686.1364171},
  interhash = {61f74fe4bb3a72220c69438010ae9962},
  intrahash = {792034671682f8720177801e2729d4c7},
  isbn = {978-1-59593-753-7},
  location = {Fortaleza, Ceara, Brazil},
  pages = {1995--1999},
  publisher = {ACM},
  title = {Tag-aware recommender systems by fusion of collaborative filtering algorithms},
  url = {http://portal.acm.org/citation.cfm?id=1364171},
  year = 2008
}

@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{herlocker2004evaluating,
  abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.},
  address = {New York, NY, USA},
  author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.},
  doi = {http://doi.acm.org/10.1145/963770.963772},
  interhash = {f8a70731d983634ac7105896d101c9d2},
  intrahash = {bdd3980bb3c297d1b84ceb0c7729d397},
  issn = {1046-8188},
  journal = {ACM Trans. Inf. Syst.},
  number = 1,
  pages = {5--53},
  publisher = {ACM Press},
  title = {Evaluating collaborative filtering recommender systems},
  url = {http://portal.acm.org/citation.cfm?id=963770.963772},
  volume = 22,
  year = 2004
}

@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
}

@inproceedings{skkr02item,
  author = {Sarwar, Badrul M. and Karypis, George and Konstan, Joseph A. and Riedl, John},
  booktitle = {Proceedings of the 10th International WWW Conference},
  ee = {http://doi.acm.org/10.1145/371920.372071},
  interhash = {043d1aaba0f0b8c01d84edd517abedaf},
  intrahash = {e2a0446da3d69b4d98da6e525e1b363f},
  pages = {285-295},
  title = {Item-based collaborative filtering recommendation algorithms},
  url = {http://dblp.uni-trier.de/db/conf/www/www2001.html#SarwarKKR01},
  year = 2001
}

@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
}