@inproceedings{koren2009collaborative,
  abstract = {Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.},
  acmid = {1557072},
  address = {New York, NY, USA},
  author = {Koren, Yehuda},
  booktitle = {Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {10.1145/1557019.1557072},
  interhash = {ca14b78afaf26db8dd7eb13d7986830a},
  intrahash = {dad3f9050f58acf0551924e537e84e45},
  isbn = {978-1-60558-495-9},
  location = {Paris, France},
  numpages = {10},
  pages = {447--456},
  publisher = {ACM},
  title = {Collaborative filtering with temporal dynamics},
  url = {http://doi.acm.org/10.1145/1557019.1557072},
  year = 2009
}

@inproceedings{bennett2007netflix,
  address = {New York},
  author = {Bennett, J. and Lanning, S.},
  booktitle = {Proceedings of the KDD Cup Workshop 2007},
  interhash = {d03b540a69c3f6f282c6f302957c5f7f},
  intrahash = {57ef9d0119acf19856b408297e5a2e5f},
  month = aug,
  pages = {3--6},
  publisher = {ACM},
  title = {The Netflix Prize},
  url = {http://www.cs.uic.edu/~liub/KDD-cup-2007/NetflixPrize-description.pdf},
  year = 2007
}

@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{bell2007lessons,
  abstract = {This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.},
  address = {New York, NY, USA},
  author = {Bell, Robert M. and Koren, Yehuda},
  doi = {10.1145/1345448.1345465},
  interhash = {e060fc1209b2dc19d58cecfc5563986b},
  intrahash = {16ae86f12fc8496399bfb3b6f3181113},
  issn = {1931-0145},
  journal = {SIGKDD Explorations Newsletter},
  month = dec,
  number = 2,
  pages = {75--79},
  publisher = {ACM},
  title = {Lessons from the Netflix prize challenge},
  url = {http://doi.acm.org/10.1145/1345448.1345465},
  volume = 9,
  year = 2007
}

@inproceedings{narayanan2008robust,
  abstract = {We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge. We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset. Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information.},
  author = {Narayanan, Arvind and Shmatikov, Vitaly},
  booktitle = {Proc. of the 29th IEEE Symposium on Security and Privacy},
  doi = {10.1109/SP.2008.33},
  interhash = {77c86be6c4bf7fc51b7faecfe85479fe},
  intrahash = {2748ba4684dbe09120aee56c6a0a9de9},
  issn = {1081-6011},
  month = may,
  pages = {111--125},
  publisher = {IEEE Computer Society},
  title = {Robust De-anonymization of Large Sparse Datasets},
  url = {http://www.cs.utexas.edu/~shmat/shmat_oak08netflix.pdf},
  year = 2008
}