Using Temporal Data for Making Recommendations

Understand

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.