Machine-Learned Model to Determine Acquisition Features Associated with a User

A computer-implemented method, comprising:

• implementing, by a computing system comprising one or more processors, one or more machine-learned models to determine one or more acquisition features associated with a user relating to a first item prior to the user interacting with the first item;
• implementing, by the computing system, the one or more machine-learned models to determine one or more experiential features relating to interactions with the first item or one or more other items;
• and
• generating a dataset based on the one or more acquisition features and the one or more experiential features.

A non-transitory computer readable medium storing instructions which, when executed by a processor, cause the processor to perform operations, the operations comprising:

• implementing one or more machine-learned models to determine one or more acquisition features associated with a user relating to a first item prior to the user interacting with the first item;
• implementing the one or more machine-learned models to determine one or more experiential features relating to interactions with the first item or one or more other items;
• and
• generating a dataset based on the one or more acquisition features and the one or more experiential features.

A computing device, comprising:

• one or more memories configured to store instructions;
• and
• one or more processors configured to execute the instructions to perform operations, the operations comprising:
• receiving a query from a user relating to a first item;
• and
• in response to receiving the query, implementing one or more machine-learned models configured to:
• determine one or more acquisition features associated with the user relating to the first item prior to the user interacting with the first item,
• determine one or more experiential features relating to interactions with the first item or one or more other items, and
• provide, as an output, a recommendation of the first item based on the one or more acquisition features and the one or more experiential features.

A computing device, comprising:

• one or more memories configured to store instructions;
• and
• one or more processors configured to execute the instructions to perform operations, the operations comprising:
• determining an occurrence of a predetermined condition with respect to a user;
• and
• in response to determining the occurrence of the predetermined condition, implementing one or more machine-learned models configured to:
• determine one or more acquisition features associated with the user relating to a first item prior to the user interacting with the first item,
• determine one or more experiential features relating to interactions with the first item or one or more other items, and
• provide, as an output, a recommendation of the first item based on the one or more acquisition features and the one or more experiential features.