Identifying augmented reality visuals influencing user behavior in virtual-commerce environments

1. A method for enhancing personalization of a virtual-commerce environment by identifying augmented-reality visual content of the virtual-commerce environment, the method comprising:

obtaining, by a processor, data indicating a plurality of augmented-reality visuals generated in the virtual-commerce environment and provided for view by a user;

obtaining, by the processor, data indicating a user input provided by the user to generate manipulated augmented-reality visuals from the plurality of augmented-reality visuals;

training, by the processor, a machine-learning algorithm usable to predict an epoch time during which users are likely to provide user inputs corresponding to a triggering user input, wherein training the machine-learning algorithm is performed using covariates generated from the user input provided by the user to generate the manipulated augmented-reality visuals;

predicting, by the processor, the epoch time by applying the machine-learning algorithm to the data indicating the user input;

identifying, by the processor, a particular manipulated augmented-reality visual of the manipulated augmented-reality visuals that is viewed by the user at or nearest the epoch time; and

storing the identified manipulated augmented-reality visual.

2. The method of claim 1 , further comprising:

identifying, by the processor and in response to determining a correspondence between the user input and the triggering user input, an additional particular augmented-reality visual provided to the user at a time associated with the user input.

3. The method of claim 2 , wherein identifying the additional particular augmented-reality visual provided to the user at the time associated with the user input further comprises:

obtaining, by the processor, a time stamp associated with each of the plurality of augmented-reality visuals;

obtaining, by the processor, a time associated with the user input corresponding to the triggering user input; and

identifying, by the processor, the particular augmented-reality visual provided to the user at the time the user provides the user input corresponding to the triggering user input based on a comparison of the time stamp associated with each of the plurality of augmented-reality visuals and the time associated with the user input corresponding to the triggering user input.

4. The method of claim 1 , further comprising:

predicting the epoch time responsive to a determination of a lack of correspondence between the user input and the triggering user input.

5. The method of claim 1 , wherein the covariates comprise the data indicating the plurality of augmented-reality visuals, the data indicating the triggering user input, and the data indicating the user input.

6. The method of claim 1 , wherein the triggering user input corresponds to user input indicating a request for information associated with a virtual object in a virtual-reality visual of the plurality of augmented-reality visuals.

7. The method of claim 1 , further comprising:

identifying, by the processor, a group of augmented-reality visuals based on the user input and the triggering user input;

obtaining, by the processor, accelerometer data associated with each identified augmented-reality visual; and

identifying, by the processor, a subset of the identified group of augmented-reality visuals based on a comparison of the accelerometer data associated with each identified augmented-reality visual and a threshold accelerometer value.

8. A system comprising:

a processing device; and

a non-transitory computer-readable medium communicatively coupled to the processing device, wherein the processing device is configured to perform operations comprising:

obtaining data indicating a plurality of augmented-reality visuals generated in a virtual-commerce environment and provided for view by a user;

obtaining data indicating a user input provided by the user to generate manipulated augmented-reality visuals from the plurality of augmented-reality visuals;

training a machine-learning algorithm usable to predict an epoch time during which users are likely to provide user inputs corresponding to a triggering user input, wherein training the machine-learning algorithm is performed using covariates generated from the user input provided by the user to generate the manipulated augmented-reality visuals;

predicting, by the processing device, the epoch time by applying the machine-learning algorithm to the data indicating the user input;

identifying a particular manipulated augmented-reality visual of the manipulated augmented-reality visuals that is viewed by the user at or nearest the epoch time; and

storing the identified manipulated augmented-reality visual.

9. The system of claim 8 , wherein the processing device is further configured to perform operations comprising:

identifying, in response to determining a correspondence between the user input and the triggering user input, an additional particular augmented-reality visual provided to the user at a time associated with the user input.

10. The system of claim 9 , wherein the operations comprising identifying the additional particular augmented-reality visual provided to the user at the time associated with the user input further comprises:

obtaining a time stamp associated with each of the plurality of augmented-reality visuals;

obtaining a time associated with the user input corresponding to the triggering user input; and

identifying the particular augmented-reality visual provided to the user at the time the user provides the user input corresponding to the triggering user input based on a comparison of the time stamp associated with each of the plurality of augmented-reality visuals and the time associated with the user input corresponding to the triggering user input.

11. The system of claim 8 , wherein predicting the epoch time is performed responsive to a determination of a lack of correspondence between the user input and the triggering user input.

12. The system of claim 8 , wherein the covariates comprise the data indicating the plurality of augmented-reality visuals, the data indicating the triggering user input, and the data indicating the user input.

13. The system of claim 8 , wherein the triggering user input corresponds to user input indicating a request for information associated with a virtual object in a virtual-reality visual of the plurality of augmented-reality visuals.

14. The system of claim 8 , wherein the processing device is further configured to perform operations comprising:

identifying a group of augmented-reality visuals based on the user input and the triggering user input;

obtaining accelerometer data associated with each identified augmented-reality visual; and

identifying a subset of the identified group of augmented-reality visuals based on a comparison of accelerometer data associated with each identified augmented-reality visual and a threshold accelerometer value.

15. A non-transitory computer-readable medium storing program code executable by a processor for enhancing personalization of a virtual-commerce environment by identifying augmented-reality visual content of the virtual-commerce environment, the program code comprising:

program code for obtaining, by the processor, data indicating a plurality of augmented-reality visuals generated in the virtual-commerce environment and provided for view by a user;

program code for obtaining, by the processor, data indicating a user input provided by the user to generate manipulated augmented-reality visuals from the plurality of augmented-reality visuals;

program code for training, by the processor, a machine-learning algorithm usable to predict an epoch time during which users are likely to provide user inputs corresponding to a triggering user input, wherein training the machine-learning algorithm is performed using at least one covariate generated from the user input provided by the user to generate the manipulated augmented-reality visuals;

program code for predicting, by the processor, the epoch time by applying the machine-learning algorithm to the data indicating the user input;

program code for identifying, by the processor, a particular manipulated augmented-reality visual of the manipulated augmented-reality visuals that is viewed by the user at or nearest the epoch time; and

program code for storing, by the processor, the identified manipulated augmented-reality visual.

16. The non-transitory computer-readable medium of claim 15 , further comprising:

program code for identifying, by the processor and in response to determining a correspondence between the user input and the triggering user input, an additional particular augmented-reality visual provided to the user at a time associated with the user input.

17. The non-transitory computer-readable medium of claim 16 , wherein the program code for identifying the additional particular augmented-reality visual based on the correspondence comprises:

program code for obtaining, by the processor, a time stamp associated with each of the plurality of augmented-reality visuals;

program code for obtaining, by the processor, a time associated with the user input corresponding to the triggering user input; and

program code for identifying, by the processor, the additional particular augmented-reality visual provided to the user at the time the user provides the user input corresponding to the triggering user input based on a comparison of the time stamp associated with each of the plurality of augmented-reality visuals and the time associated with the user input corresponding to the triggering user input.

18. The non-transitory computer-readable medium of claim 15 , wherein predicting the epoch time is performed responsive to a determination of a lack of correspondence between the user input and the triggering user input.

19. The non-transitory computer-readable medium of claim 15 , wherein the covariates comprise the data indicating the plurality of augmented-reality visuals, the data indicating the triggering user input, and the data indicating the user input.

20. The non-transitory computer-readable medium of claim 15 , wherein the program code further comprises:

program code for identifying, by the processor, a group of augmented-reality visuals based on the user input and the triggering user input;

program code for obtaining, by the processor, accelerometer data associated with each identified augmented-reality visual; and

program code for identifying, by the processor, a subset of the identified group of augmented-reality visuals based on a comparison of the accelerometer data associated with each identified augmented-reality visual and a threshold accelerometer value.