Extending machine learning training data to generate an artificial intelligence recommendation engine

1. A method, comprising:

accessing, by a processor, a catalog of physical items associated with a target user, wherein at least a portion of the catalog is at least in part automatically generated based on a retention of one or more of the physical items provided to the target user;

training, by the processor, a machine learning model using a training set of data associated with a segmented target category, wherein a plurality of users are included in the segmented target category based on a plurality of user attributes, wherein training the machine learning model includes adding or modifying one or more user attributes to the plurality of user attributes until a threshold amount of training data is included in the training set of data associated with the segmented target category, wherein the training set of data associated with the segmented target category includes using outfit combination information gathered from other users;

using, by the processor, the trained machine learning model trained to automatically determine for the target user, at least a portion of one or more recommended outfit combinations of a plurality of physical items among the physical items within the catalog; and

providing, by the processor, to the target user an indication of a selected one of the one or more recommended outfit combinations, wherein the indication of the selected one of the one or more recommended outfit combinations includes a rendering of the selected recommended outfit combination on a three-dimensional model associated with the target user.

2. The method of claim 1 , wherein the machine learning model is trained using retention data associated with at least a portion of the other users.

3. The method of claim 1 , wherein the outfit combination information gathered from the other users is a selected subset among a larger set of available outfit combination information for a group of users that includes at least the other users.

4. The method of claim 1 , wherein the outfit combination information gathered from the other users is selected for use in training the machine learning model including by identifying one or more defining features of the target user and determining the other users that share the one or more defining features.

5. The method of claim 1 , wherein the machine learning model is one of a plurality of available machine learning models and the machine learning model is selected for use based on a user segment corresponding to the target user.

6. The method of claim 5 , wherein each of the plurality of available machine learning models corresponds to different user segments.

7. The method of claim 1 , further comprising receiving a feedback of the selected one of the one or more recommended outfit combinations from the target user.

8. The method of claim 7 , wherein the feedback includes an outfit combination style preference of the target user.

9. The method of claim 7 , wherein the feedback includes a description of a modified outfit combination based on the selected one of the one or more recommended outfit combinations.

10. The method of claim 1 , further comprising:

receiving from the target user a submission describing one or more additional physical items; and

updating the catalog of physical items associated with the target user to include the one or more additional physical items.

11. The method of claim 1 , further comprising:

receiving a command to manipulate the three-dimensional model of the target user;

modifying the three-dimensional model of the target user based on the received command; and

rendering a new image of the selected recommended outfit combination on the modified three-dimensional model corresponding to a new perspective of the modified three-dimensional model.

12. The method of claim 1 , further comprising:

receiving a weather context for the target user, wherein the recommended outfit combinations are automatically determined based at least in part on the received weather context.

13. The method of claim 1 , further comprising:

receiving one or more shared calendar events of the target user, wherein the recommended outfit combinations are automatically determined based at least in part on the received one or more shared calendar events.

14. The method of claim 13 , wherein the one or more shared calendar events include a wedding, a business meeting, a vacation, or an exercise class.

15. The method of claim 1 , further comprising:

receiving a specification of a recently worn item by the target user, wherein the one or more recommended outfit combinations are automatically determined based at least in part on excluding the recently worn item from the catalog of physical items associated with the target user until a time threshold has elapsed.

16. The method of claim 1 , wherein a delivery time of the indication of the selected one of the one or more recommended outfit combinations is configured by the target user.

17. The method of claim 1 , further comprising generating a packing list of physical items corresponding to the selected one of the one or more recommended outfit combinations.

18. A method, comprising:

selecting, by a processor, a product item from an inventory based on a prediction score for a target user;

accessing, by a processor, a catalog of physical items associated with the target user, wherein at least a portion of the catalog is at least in part automatically generated based on a retention of one or more of the physical items provided to the target user;

training, by the processor, a machine learning model using a training set of data associated with a segmented target category, wherein a plurality of users are included in the segmented target category based on a plurality of user attributes, wherein training the machine learning model includes adding or modifying one or more user attributes to the plurality of user attributes until a threshold amount of training data is included in the training set of data associated with the segmented target category, wherein the training set of data associated with the segmented target category includes using outfit combination information gathered from other users;

using, by the processor, the trained machine learning model to automatically determine for the target user, at least a portion of one or more recommended outfit combinations of a plurality of physical items among the physical items within the catalog, wherein the one or more recommended outfit combinations each include the selected product item; and

providing, by the processor, to the target user the product item and an indication of a selected one of the one or more recommended outfit combinations, wherein the indication of the selected one of the one or more recommended outfit combinations includes a rendering of the selected recommended outfit combination on a three-dimensional model associated with the target user.

19. A system, comprising:

a processor configured to:

access a catalog of physical items associated with a target user, wherein at least a portion of the catalog is at least in part automatically generated based on a retention of one or more of the physical items provided to the target user;

train a machine learning model using a training set of data associated with a segmented target category, wherein a plurality of users are included in the segmented target category based on a plurality of user attributes, wherein training the machine learning model includes adding or modifying one or more user attributes to the plurality of user attributes until a threshold amount of training data is included in the training set of data associated with the segmented target category, wherein the training set of data associated with the segmented target category includes using outfit combination information gathered from other users;

use the trained machine learning model trained to automatically determine for the target user, at least a portion of one or more recommended outfit combinations of a plurality of physical items among the physical items within the catalog; and

provide to the target user an indication of a selected one of the one or more recommended outfit combinations, wherein the indication of the selected one of the one or more recommended outfit combinations includes a rendering of the selected recommended outfit combination on a three-dimensional model associated with the target user; and

a memory coupled to the processor and configured to provide the processor with instructions.