1. A method comprising: in a data processing system configured to generate personalized rankings of a first plurality of media content items in a catalog: determining a plurality of user consumption values, wherein each user consumption value of the plurality of user consumption values is associated with a user consumption data point of a plurality of user consumption data points, and wherein each user consumption data point is associated with a user and a media content item in a second plurality of media content items; determining, for each user consumption data point of the plurality of user consumption data points, a plurality of feature values associated with the user consumption data point, wherein the plurality of feature values include a particular feature value indicating whether the user associated with the user consumption data point consumed the media content item associated with the user consumption data point within a particular amount of time; training a selection value prediction model based on each feature value of the plurality of feature values associated with each user consumption data point of the plurality of user consumption data points; for each media content item of the first plurality of media content items, determining, based on the selection value prediction model, a predicted selection score for an input user and the media content item; ranking the first plurality of media content items in the catalog for the input user based on the predicted selection score for each media content item in the first plurality of media content items; wherein the method is performed using one or more processors.