Using Temporal Data for Making Recommendations
This work addresses the challenge of making recommendations by leveraging temporal order, offering incremental improvements for recommendation systems.
The paper tackles the problem of collaborative filtering by treating it as a univariate time series estimation problem, using temporal data to predict user votes, and reports improvements in predictive accuracy on real-world datasets.
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.