1. A method of a computerized media-content recommender comprising: receiving a user-judgement score for a user based on historical data regarding the user's consumption of a media content; determining, by a media-content recommender, a first prediction score for the user with respect to the media content using a first set of prediction parameters; determining a first prediction error comprising a difference between the user-judgment score and the first prediction score for the user and the media content; modifying, by at least one processor, at least one parameter value of the first set of prediction parameters with a machine-learning optimization technique to generate a second set of prediction parameters; determining, by the media-content recommender, a second prediction score for the user with respect to the media content using the second set of prediction parameters; determining a second prediction error comprising a difference between the user-judgment score and the second prediction score; identifying a lowest prediction error from the first and second prediction errors; selecting that set of prediction parameters corresponding to the lowest prediction error; and determining, for the user, a prediction score for an unconsumed media content utilizing the selected set of prediction parameters.