Using Temporal Data for Making Recommendations
Andrew Zimdars, David Chickering, 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.
PDF Link: /papers/01/p580-zimdars.pdf
AUTHOR = "Andrew Zimdars
and David Chickering and Christopher Meek",
TITLE = "Using Temporal Data for Making Recommendations",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "580--588"