Using Temporal Data for Making Recommendations
Information Retrieval
2013-01-14 v1 Artificial Intelligence
Machine Learning
Abstract
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.
Cite
@article{arxiv.1301.2320,
title = {Using Temporal Data for Making Recommendations},
author = {Andrew Zimdars and David Maxwell Chickering and Christopher Meek},
journal= {arXiv preprint arXiv:1301.2320},
year = {2013}
}
Comments
Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)