@inproceedings{zimdars01,
  abstract = {We treat collaborative filtering as a univariate time series problem: given a user’s previous votes, predict the next vote. We describe two families of methods for transforming data
to encode time order in ways amenable to of-the-shelf classication and density estimation tools. Using a decision-tree learning tool and two real-world data sets, we compare the results of these approaches to the results of
collaborative filtering without ordering information. The improvements in both predictive accuracy and in recommendation quality that we realize advocate the use of predictive
algorithms exploiting the temporal order of data.},
  address = {San Francisco, CA, USA},
  author = {Zimdars, Andrew and Chickering, David Maxwell and Meek, Christopher},
  booktitle = {UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence},
  interhash = {3dcd7aa85ac877a20275de6f4b5a14f7},
  intrahash = {46fe7be68f5b3435411beb632094a60b},
  isbn = {1-55860-800-1},
  pages = {580--588},
  publisher = {Morgan Kaufmann Publishers Inc.},
  title = {Using Temporal Data for Making Recommendations},
  url = {http://portal.acm.org/citation.cfm?id=720264},
  year = 2001
}

@article{goodword2006lowd,
  address = {Palo Alto, CA},
  author = {Lowd, Daniel and Meek, Christopher},
  booktitle = {Second Conference on Email and Anti-Spam (CEAS)},
  interhash = {c86d81bb31ea199c1d7aaf8b5e3e280d},
  intrahash = {947e546ff2a77a7f099da4955fa73df2},
  title = {Good Word Attacks on Statistical Spam Filters},
  url = {http://www.cs.washington.edu/homes/lowd/ceas05lowd.pdf},
  url1 = {http://www.cs.washington.edu/homes/lowd/ceas05lowd.ppt},
  year = 2005
}