Deep machine learning content item ranker

A system for ranking a plurality of content items for presentation to a user in response to a submission of a query, comprising:

• one or more memories;
• and
• at least one processor each coupled to at least one of the memories and configured to perform operations comprising:
• generating a ranking score for each content item in the plurality of content items, wherein the generating the ranking score comprises, for each content item of the plurality of content items:
• providing input to a deep machine learning (ML) model, the input including at least one or more query features associated with the query and one or more content item features associated with the content item;
• determining, by a first task-specific multi-layer perceptron (MLP) of the deep ML model and based at least on the one or more query features and the one or more content item features, a first probability of a first type of user-item interaction between the user and the content item when the content item is displayed on a user interface (UI) of a media device;
• determining, by a second task-specific MLP of the deep ML model and based at least on the one or more query features and the one or more content item features, a second probability of a second type of user-item interaction between the user and the content item when the content item is displayed on the UI of the media device;
• and
• calculating the ranking score for the content item based at least on the first probability and the second probability;
• and
• ranking the plurality of content items for presentation to the user based on the ranking score associated with each content item in the plurality of content items.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations for ranking a plurality of content items for presentation to a user in response to a submission of a query, the operations comprising:

• generating a ranking score for each content item in the plurality of content items, wherein the generating the ranking score comprises, for each content item of the plurality of content items:
• providing input to a deep machine learning (ML) model, the input including at least one or more query features associated with the query and one or more content item features associated with the content item;
• determining, by a first task-specific multi-layer perceptron (MLP) of the deep ML model and based at least on the one or more query features and the one or more content item features, a first probability of a first type of user-item interaction between the user and the content item when the content item is displayed on a user interface (UI) of a media device;
• determining, by a second task-specific MLP of the deep ML model and based at least on the one or more query features and the one or more content item features, a second probability of a second type of user-item interaction between the user and the content item when the content item is displayed on the UI of the media device;
• and
• calculating the ranking score for the content item based at least on the first probability and the second probability;
• and
• ranking the plurality of content items for presentation to the user based on the ranking score associated with each content item in the plurality of content items.