Using Temporal Data for Making Recommendations

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.

Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)

References in corpus (3)

• Empirical Analysis of Predictive Algorithms for Collaborative Filtering
• A Bayesian Approach to Learning Bayesian Networks with Local Structure
• Fast Learning from Sparse Data

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