  We treat collaborative filtering as a univariate time series estimation
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 off-the-shelf classification and density estimation tools, and examine the
results of using these approaches on several real-world data sets. The
improvements in predictive accuracy we realize recommend the use of other
predictive algorithms that exploit the temporal order of data.
