Item recommendation with application to automated artificial intelligence

A computing system for selecting preferred machine learning pipelines for processing new datasets, the system comprising:

• a memory subsystem, with program instructions included thereon;
• and
• a processor in communication with the memory subsystem, wherein the program instructions cause the processor to:
• for a plurality of machine learning pipelines and a plurality N of datasets previously-processed by the pipelines, store a plurality of rating values, each rating value corresponding to a performance of a pipeline of the plurality of machine learning pipelines and for a dataset of the plurality N of datasets;
• for each pair ui=1 to N, uj=1 to N, i±j of the plurality N of previously-processed datasets, determine a distance di,j from ui to uj in a latent space, wherein the distance di,j corresponds to an expected value of a regret incurred when the pipeline, selected in a predetermined manner based on a set of rating values for a dataset uj, is rated for a performance of the selected pipeline for a dataset ui, and wherein the regret for the selected pipeline includes a monotonically decreasing function of the rating value for the performance of the pipeline for the dataset ui,
• cluster the plurality N of previously-processed datasets in the latent space and identify a representative dataset in each cluster for which each distance to the dataset from other datasets in the cluster is minimized over the cluster;
• in response to receiving a new dataset, select a set of preferred pipelines from the plurality of machine learning pipelines for processing the new dataset, each preferred pipeline being selected according to a set of rating values for the representative dataset;
• and
• process the new dataset in the set of preferred pipelines.

A computer program product for selecting preferred machine learning pipelines for processing new datasets, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to:

• for a plurality of machine learning pipelines and a plurality N of datasets previously-processed by the pipelines, store a plurality of rating values, each rating value corresponding to a performance of a pipeline of the plurality of machine learning pipelines and for a dataset of the plurality N of datasets;
• for each pair ui=1 to N, uj=1 to N, i±j of the plurality N of previously-processed datasets, determine a distance di,j from ui to uj in a latent space, wherein the distance di,j corresponds to an expected value of a regret incurred when the pipeline, selected in a predetermined manner based on a set of rating values for a dataset uj, is rated for a performance of the selected pipeline for a dataset ui, and wherein the regret for the selected pipeline includes a monotonically decreasing function of the rating value for the performance of the pipeline for the dataset ui;
• cluster the plurality N of previously-processed datasets in the latent space and identify a representative dataset in each cluster for which each distance to the dataset from other datasets in the cluster is minimized over the cluster;
• in response to receiving a new dataset, select a set of preferred pipelines from the plurality of machine learning pipelines for processing the new dataset, each preferred pipeline being selected according to a set of rating values for the representative dataset;
• and
• process the new dataset in the set of preferred pipelines.