Methods and systems for biologically determined artificial intelligence selection guidance

1. A system for biologically determined artificial intelligence selection guidance, the system comprising a computing device designed and configured to:

receive at least a biological extraction and an item descriptor from a user;

generate, using a classification algorithm and a plurality of past extractions, a user classifier matching user data to user sets as a function of the at least a biological extraction;

identify, using the user classifier and the user data, a user set identifier matching the user;

classify the item descriptor to a negative behavior of the user using a vice classifier, wherein the vice classifier is trained with training data correlating user data and item descriptors to user negative behaviors;

produce a selection guidance using the user set identifier and the item descriptor;

calculate, as a function of the negative behavior of the user, an alternate item of the selection guidance; and

provide the selection guidance to the user.

2. The system of claim 1 , wherein the at least a biological extraction further comprises a profile generated using previous item selections by the user.

3. The system of claim 1 , wherein the item descriptor includes an item selection.

4. The system of claim 1 , wherein the item descriptor includes an item category identifier.

5. The system of claim 1 , wherein the computing device is configured to produce the selection guidance by:

identifying at least an action associated with the item descriptor, the at least an action identified as having been performed by other users classified to the user set;

determining at least a selection associated with the at least an action; and

producing the selection guidance using the at least a selection.

6. The system of claim 1 , wherein the computing device is further configured to rank the selection guidance according to at least a selection criterion.

7. The system of claim 6 , wherein ranking further comprises:

calculating a loss function of the at least a selection criterion; and

ranking selections to minimize the loss function.

8. The system of claim 1 , wherein the computing device is further configured to:

generate at least a machine-learning model that generates selection suitability determinations using the biological extraction; and

generate a selection suitability determination for the item descriptor using the at least a machine-learning model.

9. The system of claim 8 , wherein the model generates an item suitability score using biological extraction and an item attribute list.

10. The system of claim 9 , where the computing device is further configured to determine that the item descriptor is not suitable, and wherein generating the selection guidance further comprises generating a recommendation of a different item.

11. A method of biologically determined artificial intelligence selection guidance, the method comprising:

receiving, by a computing device, at least a biological extraction and an item descriptor from a user;

generating, by the computing device and using a classification algorithm and a plurality of past extractions, a user classifier matching user data to user sets as a function of the at least a biological extraction;

identifying, by the computing device, using the user classifier and the user data, a user set identifier matching the user;

classifying, the item descriptor to a negative behavior of the user using a vice classifier, wherein the vice classifier is trained with training data correlating user data and item descriptors to user negative behaviors;

producing, by the computing device a selection guidance using the user set identifier and the item descriptor;

calculating, as a function of the negative behavior of the user, an alternate item of the selection guidance; and

providing, by the computing device, the selection guidance to the user.

12. The method of claim 11 , wherein the at least a biological extraction further comprises a profile generated using previous item selections by the user.

13. The method of claim 11 , wherein the item descriptor includes an item selection.

14. The method of claim 11 , wherein the item descriptor includes an item category identifier.

15. The method of claim 11 , wherein producing the selection guidance further comprises:

identifying at least an action associated with the item descriptor, the at least an action identified as having been performed by other users classified to the user set;

determining at least a selection associated with the at least an action; and

producing the selection guidance using the at least a selection.

16. The method of claim 1 further comprising ranking the selection guidance according to at least a selection criterion.

17. The method of claim 16 , wherein ranking further comprises:

calculating a loss function of the at least a selection criterion; and

ranking selections to minimize the loss function.

18. The method of claim 1 , further comprising:

generating at least a machine-learning model that generates selection suitability determinations using the biological extraction; and

generating a selection suitability determination for the item descriptor using the at least a machine-learning model.

19. The method of claim 18 , wherein the model generates an item suitability score using biological extraction and an item attribute list.

20. The method of claim 19 , further comprising determining that the item descriptor is not suitable, and wherein generating the selection guidance further comprises generating a recommendation of a different item.