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.
|Published in||Proceedings of Seventeenth Conference on Uncertainty in Artificial Intelligence, ® Seattle, WA|
|Publisher||Morgan Kaufmann Publishers|