Abstract: Systems and methods for intuitive search and recommendation including a content comprehension engine executing on a computer processor and configured to: receive a recommendation request identifying a source content item; generate a first embedding for the source content item in a first embedding space from content metadata and contextual data; apply a trained neural projection model to map the first embedding to a second embedding space, thereby producing a projected embedding; compute, for content item models stored in a repository, a similarity score between the projected embedding and the content item model, each content item model including word-vector collaborative-filtering representations of an available content item; select, based on the similarity scores, a subset of the content item models; and output a result set including the available content items corresponding to the subset and ordered by the similarity scores.

Abstract: System and methods for intuitive search operation results using machine learning including: identifying a first candidate content item matching a content item search request; identifying a first content item model corresponding to the first candidate content item including word vector collaborative filtering representations of the first candidate content item; identifying a set of content item models where each: is associated with at least one corresponding available content item, and includes word vector collaborative filtering representations; applying deep neural learning to compare the first content item model with the set of content item models to generate a subset of the content item models most relevant to the first content item model; generating a result set of available content items corresponding to the subset of the content item models most relevant to the first content item model; and providing the result set of available content items.

Abstract: System and methods for intuitive search operation results using machine learning including: identifying a first candidate content item matching a content item search request; identifying a first content item model corresponding to the first candidate content item including word vector collaborative filtering representations of the first candidate content item; identifying a set of content item models where each: is associated with at least one corresponding available content item, and includes word vector collaborative filtering representations; applying deep neural learning to compare the first content item model with the set of content item models to generate a subset of the content item models most relevant to the first content item model; generating a result set of available content items corresponding to the subset of the content item models most relevant to the first content item model; and providing the result set of available content items.

Abstract: System and methods for cold-starting content on a platform using machine learning including: identifying content metadata and contextual data both corresponding to a target content item; generating a target content item model by applying deep neural learning that: 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; applying deep neural learning to compare the target content item model with a 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.