@article{breiman2001random,
  abstract = {Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to },
  author = {Breiman, Leo},
  doi = {10.1023/A:1010933404324},
  interhash = {4450d2e56555e7cb8f3817578e1dd4da},
  intrahash = {b8187107bf870043f2f93669958858f1},
  issn = {0885-6125},
  journal = {Machine Learning},
  language = {English},
  number = 1,
  pages = {5-32},
  publisher = {Kluwer Academic Publishers},
  title = {Random Forests},
  url = {http://dx.doi.org/10.1023/A%3A1010933404324},
  volume = 45,
  year = 2001
}

@inproceedings{dong2009overview,
  author = {Dong, Xishuang and Chen, Xiaodong and Guan, Yi and Yu, Zhiming and Li, Sheng},
  booktitle = {CSIE (3)},
  crossref = {conf/csie/2009},
  editor = {Burgin, Mark and Chowdhury, Masud H. and Ham, Chan H. and Ludwig, Simone A. and Su, Weilian and Yenduri, Sumanth},
  ee = {http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.1090},
  interhash = {038285e30e929088afad8d82c066ef75},
  intrahash = {d970cfabe05f5e19100099afa11b9873},
  isbn = {978-0-7695-3507-4},
  pages = {600-606},
  publisher = {IEEE Computer Society},
  title = {An Overview of Learning to Rank for Information Retrieval.},
  url = {http://dblp.uni-trier.de/db/conf/csie/csie2009-3.html#DongCGYL09},
  year = 2009
}

@inproceedings{rendle2009learning,
  abstract = {Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.},
  address = {New York, NY, USA},
  author = {Rendle, Steffen and Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars},
  booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {10.1145/1557019.1557100},
  interhash = {1cc85ca2ec82db2a3caf40fd1795a58a},
  intrahash = {1bd672ffb8d6ba5589bb0c7deca09412},
  isbn = {978-1-60558-495-9},
  location = {Paris, France},
  pages = {727--736},
  publisher = {ACM},
  title = {Learning optimal ranking with tensor factorization for tag recommendation},
  url = {http://portal.acm.org/citation.cfm?doid=1557019.1557100},
  year = 2009
}