Abstract: In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.

Abstract: Technologies for scoring and ranking cohorts containing content items using a machine-learned model are provided. The disclosed techniques include a cross-cohort optimization system that stores, within memory, cohort definition criteria for each cohort of a plurality of cohorts. The optimization system, for a particular user, for each cohort, identifies a plurality of content items that belong to the specific cohort based upon the cohort definition criteria. Using a machine-learned model, the optimization system generates a score for the specific cohort with respect to the particular user's intentions. The optimization system generates a ranking for the plurality of cohorts based on the respective scores of each cohort. The optimization system causes the plurality of content items of each cohort to be displayed concurrently on a computing device of the particular user. Display order for the plurality of cohorts is based on the ranking determined for the plurality of cohorts.

Abstract: In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.

Abstract: Technologies for scoring and ranking cohorts containing content items using a machine-learned model are provided. The disclosed techniques include a cross-cohort optimization system that stores, within memory, cohort definition criteria for each cohort of a plurality of cohorts. The optimization system, for a particular user, for each cohort, identifies a plurality of content items that belong to the specific cohort based upon the cohort definition criteria. Using a machine-learned model, the optimization system generates a score for the specific cohort with respect to the particular user's intentions. The optimization system generates a ranking for the plurality of cohorts based on the respective scores of each cohort. The optimization system causes the plurality of content items of each cohort to be displayed concurrently on a computing device of the particular user. Display order for the plurality of cohorts is based on the ranking determined for the plurality of cohorts.