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In this thesis, we study the problem of predicting users' media preferences based on their, as well as other users' historical rating data. The model used is that a user's rating for an item can be explained by a sum of bias terms and an inner product between two vectors in some multidimensional feature space that is specic to the product domain. This model is tted using a stochastic d
