1. A method comprising: defining, by a computing device, information for a set of genomes, the set of genomes describing characteristics of media programs; defining, by the computing device, which genomes in the set of genomes correspond to which topics in a set of topics; inputting, by the computing device, textual information for a plurality of media programs and the information for the set of genomes into a model; training, by the computing device, the model to determine a probability distribution of terms for the set of topics based on analyzing the textual information and the information for the set of genomes, wherein training comprises incorporation a parameter that prevents topics from being submerged by other topics that are larger via a first item in the model; and outputting, by the computing device, the trained model, wherein the probability distribution of terms is usable to determine genomes for each of the plurality of media programs, wherein a genome corresponds to a topic and is associated with a media program based on terms found in the textual information for the media program and the probability distribution of terms for the topic corresponding to the genome.
1. A method comprising: defining, by a computing device, information for a set of genomes, the set of genomes describing characteristics of media programs; defining, by the computing device, which genomes in the set of genomes correspond to which topics in a set of topics; inputting, by the computing device, textual information for a plurality of media programs and the information for the set of genomes into a model; training, by the computing device, the model to determine a probability distribution of terms for the set of topics based on analyzing the textual information and the information for the set of genomes, wherein training comprises incorporation a parameter that prevents topics from being submerged by other topics that are larger via a first item in the model; and outputting, by the computing device, the trained model, wherein the probability distribution of terms is usable to determine genomes for each of the plurality of media programs, wherein a genome corresponds to a topic and is associated with a media program based on terms found in the textual information for the media program and the probability distribution of terms for the topic corresponding to the genome. 6. The method of claim 1 , wherein a joint distribution function of a plurality of media programs and the genomes is determined.