Recommending edges via importance aware machine learned model

A computerized method comprising:

• training a first cohort-specific machine learned pre diction model corresponding to a first cohort by specifying a first optimization parameter and then feeding first training data and the first optimization parameter into a first machine learning algorithm, the first training data including sample entities of a first type of entity in an online network and sample entries of a first type of connection between entities in the online network;
• receiving an identification of a first user in an online network;
• obtaining, from the first cohort-specific machine learned prediction model, first scores for a plurality of different entities of the first cohort based on the identification of the first user, the first scores indicative of a likelihood of the first user interacting with each of the plurality of different entities of the first cohort via a graphical user interface of the online network, wherein the first cohort is a first combination of the first type of entity in an online network and the first type of connection between entities in the online network, the first cohort-specific machine learned prediction model calculating first scores on a first scale;
• calibrating the first scores onto a common scale utilized by multiple cohort-specific machine learned prediction models;
• obtaining, from a second cohort-specific machine learned prediction model corresponding to a second cohort based on the identification of the first user, second scores for a plurality of different entities of the second cohort, the second scores indicative of a likelihood of the first user interacting with each of the plurality of different entities of the second cohort via a graphical user interface of the online network, wherein the second cohort is a second combination of the first type of entity in the online network and a second type of connection between entities in the online network, the second cohort-specific machine learned prediction model calculating the second scores on a second scale, wherein the second cohort-specific machine learned prediction model is trained separately from the first cohort-specific machine learned prediction model;
• calibrating the second scores onto the common scale;
• feeding the identification of the first user, the calibrated first scores, and the calibrated second scores into a single calibrated importance-aware machine learned model trained to predict a probability of downstream interaction by users with top-k entities within each of a plurality of cohorts to obtain a ranking of the plurality of cohorts for the first user;
• and
• causing display, in the graphical user interface, to the first user, of one or more entities of the first cohort and one or more entities of the second cohort in a manner that entities of a higher ranking cohort are displayed above entities of a lower ranking cohort.

A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:

• training a first cohort-specific machine learned pre diction model corresponding to a first cohort by specifying a first optimization parameter and then feeding first training data and the first optimization parameter into a first machine learning algorithm, the first training data including sample entities of a first type of entity in an online network and sample entries of a first type of connection between entities in the online network;
• receiving an identification of a first user in an online network;
• obtaining, from the first cohort-specific machine learned prediction model, first scores for a plurality of different entities of the first cohort based on the identification of the first user, the first scores indicative of a likelihood of the first user interacting with each of the plurality of different entities of the first cohort via a graphical user interface of the online network, wherein the first cohort is a first combination of the first type of entity in an online network and the first type of connection between entities in the online network, the first cohort-specific machine learned prediction model calculating first scores on a first scale;
• calibrating the first scores onto a common scale utilized by multiple cohort-specific machine learned prediction models;
• obtaining, from a second cohort-specific machine learned prediction model corresponding to a second cohort based on the identification of the first user, second scores for a plurality of different entities of the second cohort, the second scores indicative of a likelihood of the first user interacting with each of the plurality of different entities of the second cohort via a graphical user interface of the online network, wherein the second cohort is a second combination of the first type of entity in the online network and a second type of connection between entities in the online network, the second cohort-specific machine learned prediction model calculating the second scores on a second scale, wherein the second cohort-specific machine learned prediction model is trained separately from the first cohort-specific machine learned prediction model;
• calibrating the second scores onto the common scale;
• feeding the identification of the first user, the calibrated first scores, and the calibrated second scores into a single calibrated importance-aware machine learned model trained to predict a probability of downstream interaction by users with top-k entities within each of a plurality of cohorts to obtain a ranking of the plurality of cohorts for the first user;
• and
• causing display, in the graphical user interface, to the first user, of one or more entities of the first cohort and one or more entities of the second cohort in a manner that entities of a higher ranking cohort are displayed above entities of a lower ranking cohort.