A method comprising, by one or more computing systems: accessing a first data matrix comprising a plurality of row data and a plurality of column data; providing, to a first generative adversarial network, GAN, a first data input comprising a plurality of row vectors corresponding to the plurality of row data from the first data matrix; providing, to a second GAN, a second data input comprising a plurality of column vectors corresponding to the plurality of column data from the first data matrix; and generating, by simultaneous co-clustering the plurality of row vectors and the plurality of column vectors by the first GAN and the second GAN, a co-clustered correlation matrix based at least in part on the plurality of row vectors and the plurality of column vectors, wherein the co-clustered correlation matrix comprises co-clustered associations between the plurality of row data and the plurality of column data of the first data matrix, wherein the first GAN and the second GAN are part of a co-informatic generative adversarial network, CI-GAN, autoencoder model architecture, wherein the CI-GAN autoencoder model architecture further comprises a distribution function configured to reconstruct the plurality of row data and the plurality of column data based on a plurality of output row data generated by the first GAN and a plurality of output column data generated by the second GAN, and wherein the generating, by simultaneous co-clustering the plurality of row vectors and the plurality of column vectors by the first GAN and the second GAN, the co-clustered correlation matrix comprises: determining one or more underlying interrelations between the plurality of row data and the plurality of column data of the first data matrix based at least in part on a mutual information cost function, wherein the first data matrix comprises user behavioral data as the plurality of row data and user content viewing data as the plurality of column data, and generating the co-clustered correlation matrix based on the determined one or more underlying interrelations, and providing, by a network-based content orchestrator of the one or more computing systems, TV programming and recommendations content to an end-user client for user viewing, wherein the TV programming and recommendations content are based on the one or more underlying interrelations. accessing a first data matrix comprising a plurality of row data and a plurality of column data; providing, to a first generative adversarial network, GAN, a first data input comprising a plurality of row vectors corresponding to the plurality of row data from the first data matrix; providing, to a second GAN, a second data input comprising a plurality of column vectors corresponding to the plurality of column data from the first data matrix; and generating, by simultaneous co-clustering the plurality of row vectors and the plurality of column vectors by the first GAN and the second GAN, a co-clustered correlation matrix based at least in part on the plurality of row vectors and the plurality of column vectors, wherein the co-clustered correlation matrix comprises co-clustered associations between the plurality of row data and the plurality of column data of the first data matrix, wherein the first GAN and the second GAN are part of a co-informatic generative adversarial network, CI-GAN, autoencoder model architecture, wherein the CI-GAN autoencoder model architecture further comprises a distribution function configured to reconstruct the plurality of row data and the plurality of column data based on a plurality of output row data generated by the first GAN and a plurality of output column data generated by the second GAN, and wherein the generating, by simultaneous co-clustering the plurality of row vectors and the plurality of column vectors by the first GAN and the second GAN, the co-clustered correlation matrix comprises: determining one or more underlying interrelations between the plurality of row data and the plurality of column data of the first data matrix based at least in part on a mutual information cost function, wherein the first data matrix comprises user behavioral data as the plurality of row data and user content viewing data as the plurality of column data, and generating the co-clustered correlation matrix based on the determined one or more underlying interrelations, and determining one or more underlying interrelations between the plurality of row data and the plurality of column data of the first data matrix based at least in part on a mutual information cost function, wherein the first data matrix comprises user behavioral data as the plurality of row data and user content viewing data as the plurality of column data, and generating the co-clustered correlation matrix based on the determined one or more underlying interrelations, and providing, by a network-based content orchestrator of the one or more computing systems, TV programming and recommendations content to an end-user client for user viewing, wherein the TV programming and recommendations content are based on the one or more underlying interrelations.