Content cold-start machine learning system

A method for cold-starting content on a platform using machine learning, the method comprising:

• identifying a target content item to be modeled;
• identifying content metadata and contextual data both corresponding to the target content item;
• generating, by at least one computer processor, a target content item model by applying deep neural learning to the content metadata and the contextual data, wherein the deep neural learning:
• applies a word vector embedding operation to the content metadata to generate a collaborative filtering representation of the content metadata,
• applies a word vector embedding operation to the contextual data to generate a collaborative filtering representation of the contextual data, and
• bridges the collaborative filtering representations of the content metadata and the contextual data to generate the target content item model;
• identifying a set of existing content item models, wherein each of the set of existing content item models is associated with at least one corresponding known content item;
• applying deep neural learning to compare the target content item model with the set of existing content item models;
• determining cold-start characteristics of the target content item based on the comparison;
• and
• providing the cold-start characteristics for distribution management of the target content item.

A non-transitory computer-readable storage medium comprising a plurality of instructions for cold-starting content on a platform using machine learning, the plurality of instructions configured to execute on at least one computer processor to enable the at least one computer processor to:

• identify a target content item to be modeled;
• identify content metadata and contextual data both corresponding to the target content item;
• generate a target content item model by applying deep neural learning to the content metadata and the contextual data, wherein the deep neural learning:
• applies a word vector embedding operation to the content metadata to generate a collaborative filtering representation of the content metadata,
• applies a word vector embedding operation to the contextual data to generate a collaborative filtering representation of the contextual data, and
• bridges the collaborative filtering representations of the content metadata and the contextual data to generate the target content item model;
• identify a set of existing content item models, wherein each of the set of existing content item models is associated with at least one corresponding known content item;
• apply deep neural learning to compare the target content item model with the set of existing content item models;
• determine cold-start characteristics of the target content item based on the comparison;
• and
• provide the cold-start characteristics for distribution management of the target content item.