Using Temporal Data for Making Recommendations

TL;DR

This paper models collaborative filtering as a time series prediction problem, introducing data transformation methods to incorporate temporal order, leading to improved recommendation accuracy on real-world datasets.

Contribution

It proposes two data transformation techniques to encode temporal information for collaborative filtering, enhancing predictive performance using existing classification tools.

Findings

Temporal encoding improves recommendation accuracy

Transformations enable use of standard classification algorithms

Results on real datasets show significant gains

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.