In this paper, we study the behavior of collaborative filtering based recommendations under evolving user profile scenarios. We propose a systematic validation methodology that allows for simulating various controlled user profile evolution scenarios and validating the studied recommendation strategies. Through the presented work, we observe the effect of the curse of dimensionality and sparsity that can wreck havoc on collaborative filtering in a streaming scenario, and conclude that a hybrid approach with both content and collaborative filtering may be the way to go in a high sparsity streaming scenario.

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