@article{bennett2007workshop,
  abstract = {The KDD Cup is the oldest of the many data mining competitions that are now popular. It is an integral part of the annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). In 2007, the traditional KDD Cup competition was augmented with a workshop with a focus on the concurrently active Netflix Prize competition. The KDD Cup itself in 2007 consisted of a prediction competition using Netflix movie rating data, with tasks that were different and separate from those being used in the Netflix Prize itself. At the workshop, participants in both the KDD Cup and the Netflix Prize competition presented their results and analyses, and exchanged ideas.},
  address = {New York, NY, USA},
  author = {Bennett, James and Elkan, Charles and Liu, Bing and Smyth, Padhraic and Tikk, Domonkos},
  doi = {10.1145/1345448.1345459},
  interhash = {268d74a2e593d3706b883af83e7ad5bd},
  intrahash = {83613ba2e5509adfe7497aaeee069149},
  issn = {1931-0145},
  journal = {SIGKDD Explorations Newsletter},
  month = dec,
  number = 2,
  pages = {51--52},
  publisher = {ACM},
  title = {KDD Cup and workshop 2007},
  url = {http://doi.acm.org/10.1145/1345448.1345459},
  volume = 9,
  year = 2007
}

@article{takacs2009scalable,
  abstract = {The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.},
  acmid = {1577091},
  author = {Takács, Gábor and Pilászy, István and Németh, Bottyán and Tikk, Domonkos},
  interhash = {cd0d01e922dbd5b178e8e5b0a4d1e96c},
  intrahash = {1f1be967aed57e6e42a5d99ca98584cd},
  issn = {1532-4435},
  journal = {Journal of Machine Learning Research},
  month = jun,
  numpages = {34},
  pages = {623--656},
  publisher = {JMLR.org},
  title = {Scalable Collaborative Filtering Approaches for Large Recommender Systems},
  url = {http://dl.acm.org/citation.cfm?id=1577069.1577091},
  volume = 10,
  year = 2009
}

@inproceedings{conf/recsys/SaidTH12,
  author = {Said, Alan and Tikk, Domonkos and Hotho, Andreas},
  booktitle = {RecSys},
  crossref = {conf/recsys/2012},
  editor = {Cunningham, Padraig and Hurley, Neil J. and Guy, Ido and Anand, Sarabjot Singh},
  ee = {http://doi.acm.org/10.1145/2365952.2365959},
  interhash = {ae25f45fcaa24193dee76098896cab86},
  intrahash = {e030cb4883f2e989a903ed65d0a13308},
  isbn = {978-1-4503-1270-7},
  pages = {9-10},
  publisher = {ACM},
  title = {The challenge of recommender systems challenges.},
  url = {http://dblp.uni-trier.de/db/conf/recsys/recsys2012.html#SaidTH12},
  year = 2012
}