Dynamic question recommendation

A method comprising:

• receiving a request, via a graphical user interface, to add a new object to a directory of objects, the new object having a first category in a hierarchical taxonomy of categories and objects;
• retrieving one or more questions previously assigned prior to the receiving the request, to the first category and/or one or more existing objects within the first category;
• feeding each of the retrieved one or more questions and information about the new object into a first machine learned model trained to output a probability that a question is applicable to an object, wherein the first machine learned model is trained by:
• feeding one or more sample questions and associated objects into a feature extractor designed to extract one or more features from the one or more sample questions and associated objects;
• and
• passing the extracted one or more features and associated labels into a first machine learning algorithm to train the first machine learned model;
• generating one or more questions for the new object based on the probability for each of the retrieved one or more questions;
• assigning at least one of the one or more generated questions to the new object;
• and
• causing presentation of one or more of the one or more generated questions to a user for selection.

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:

• receiving a request, via a graphical user interface, to add a new object to a directory of objects, the new object having a first category in a hierarchical taxonomy of categories and objects;
• retrieving one or more questions previously assigned prior to the receiving the request, to the first category and/or one or more existing objects within the first category;
• feeding each of the retrieved one or more questions and information about the new object into a first machine learned model trained to output a probability that a question is applicable to an object, wherein the first machine learned model is trained by:
• feeding one or more sample questions and associated objects into a feature extractor designed to extract one or more features from the one or more sample questions and associated objects;
• and
• passing the extracted one or more features and associated labels into a first machine learning algorithm to train the first machine learned model;
• generating one or more questions for the new object based on the probability for each of the retrieved one or more questions;
• assigning at least one of the one or more generated questions to the new object;
• and
• causing presentation of one or more of the one or more generated questions to a user for selection.