@article{koren2009matrix,
  abstract = {As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.},
  author = {Koren, Y. and Bell, R. and Volinsky, C.},
  doi = {10.1109/MC.2009.263},
  interhash = {cface72aeba6ee8c561ccd15035d0ead},
  intrahash = {59ab9b2678949949c04b0fe2a431585a},
  issn = {0018-9162},
  journal = {Computer},
  month = aug,
  number = 8,
  pages = {30--37},
  title = {Matrix Factorization Techniques for Recommender Systems},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5197422&tag=1},
  volume = 42,
  year = 2009
}

@inproceedings{rendle2010pairwise,
  abstract = {Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.},
  acmid = {1718498},
  address = {New York, NY, USA},
  author = {Rendle, Steffen and Schmidt-Thieme, Lars},
  booktitle = {Proceedings of the third ACM international conference on Web search and data mining},
  doi = {10.1145/1718487.1718498},
  interhash = {ce8fbdf2afb954579cdb58104fb683a7},
  intrahash = {10fe730b391b08031f3103f9cdbb6e1a},
  isbn = {978-1-60558-889-6},
  location = {New York, New York, USA},
  numpages = {10},
  pages = {81--90},
  publisher = {ACM},
  title = {Pairwise interaction tensor factorization for personalized tag recommendation},
  url = {http://doi.acm.org/10.1145/1718487.1718498},
  year = 2010
}