Automated machine learning to generate recommendations for websites or applications

A computer-implemented method comprising:

• receiving a recommendation specification that includes a content type and an outcome identifier;
• determining one or more model parameters for a machine learning model based at least in part on the recommendation specification;
• generating a historical user feature matrix (FM) based on historical user data;
• generating a historical content feature matrix (FM) based on historical content data;
• obtaining, using the machine learning model, a plurality of user clusters based on the historical user FM;
• partitioning each user cluster of the plurality of user clusters into two sets based on the historical content FM, wherein a first set includes user identifiers that were associated with a positive result for the outcome identifier for at least one content item of the content type and a second set includes user identifiers that were not associated with the positive result for the outcome identifier in the historical user data;
• and
• after the partitioning, obtaining a list of content items of the content type for each cluster, wherein the content items in the list are ranked based on a respective score.

A computer-implemented method comprising:

• receiving a request for a recommendation of one or more content items of a content type, wherein the request includes a user identifier;
• assigning the user identifier to one of a treatment group or a control group;
• if the user identifier is assigned to the treatment group,
• identifying a ranked list of content items of the content type by applying a machine learning model, wherein the user identifier is an input to the machine learning model;
• and
• providing one or more content items of the ranked list of content items as the recommendation;
• if the user identifier is assigned to the control group,
• selecting a set of content items without use of the machine learning model;
• and
• providing one or more content items from the set of content items as the recommendation;
• and
• determining a result of the recommendation based on one or more subsequent user actions.