Using Temporal Data for Making Recommendations
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UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, Seite 580--588. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (2001)

We treat collaborative filtering as a univariate time series 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 of-the-shelf classication and density estimation tools. Using a decision-tree learning tool and two real-world data sets, we compare the results of these approaches to the results of collaborative filtering without ordering information. The improvements in both predictive accuracy and in recommendation quality that we realize advocate the use of predictive algorithms exploiting the temporal order of data.
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