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Using Temporal Data for Making Recommendations

Andrew Zimdars, David Maxwell Chickering, and Christopher Meek


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.


Publication typeInproceedings
Published inProceedings of Seventeenth Conference on Uncertainty in Artificial Intelligence, ® Seattle, WA
PublisherMorgan Kaufmann Publishers
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