System for recommending a meal kit based upon a user food item purchase history and related methods

1. A system for recommending a meal kit comprising:

a user device associated with a given user; and

a meal kit recommendation server configured to

obtain a food item purchase history associated with the given user,

obtain a current geographic location of the given user based upon the user device,

obtain a preferred store location from among a plurality of store locations based upon the current geographic location,

provide the food item purchase history and use-by dates of food ingredients as inputs to a training model and apply machine learning to generate a recommended meal kit for purchase at the preferred store location, the meal kit comprising a recipe and corresponding ones of the food ingredients portioned for the recipe, each of the food ingredients being purchasable individually in portions not corresponding to the recipe, the recommended meal kit being generated so that at least one of the corresponding food ingredients at the preferred store location is stocked at a relatively high quantity relative to other ones of the plurality of store locations and has a use-by date closer than other food ingredients relative to a current date,

generate a digital promotion for the recommended meal kit, the digital promotion having a promotional value associated therewith,

adjust the promotional value based upon the current location of the given user and the corresponding use dates so that the promotional value increases with distance of the given user from the preferred store location and so that the promotional value increases closer to the use-by dates relative to the current date, and

communicate the recommended meal kit and the digital promotion to the user device.

2. The system of claim 1 wherein the meal kit recommendation server is configured to determine a meal serving size based upon quantities of a given food item from the food item purchase history, and generate the recommended meal kit to have a meal kit serving size based upon the meal serving size.

3. The system of claim 1 wherein the meal kit recommendation server is configured to determine historical food ingredients from the food item purchase history and generate the recommended meal kit to include the food ingredients portioned for the recipe based upon the historical food ingredients.

4. The system of claim 1 wherein the meal kit recommendation server is configured to determine at least one food type based upon the food item purchase history and generate the recommended meal kit based upon the at least one food type.

5. The system of claim 1 wherein the meal kit recommendation server is configured to obtain the food item purchase history based upon a user identifier associated with a given user.

6. The system of claim 5 wherein the meal kit recommendation server is configured to cooperate with the user device to obtain the user identifier.

7. The system of claim 6 wherein the user identifier comprises at least one of a user name, user phone number, user email address, and user loyalty account number.

8. The system of claim 1 wherein the meal kit recommendation server is configured to determine food items being purchased during a purchase transaction and generate the recommended meal kit based upon the food items being purchased during the purchase transaction.

9. The system of claim 8 further comprising a point-of-sale (POS) terminal; and wherein the meal kit recommendation server is configured to cooperate with the POS terminal to determine the food items being purchased.

10. The system of claim 8 wherein the meal kit recommendation server is configured to determine the food items being purchased during a purchase transaction based upon an electronic shopping cart associated with the user device.

11. A meal kit recommendation server comprising:

a processor and an associated memory configured to

obtain a food item purchase history associated with a given user,

obtain a current geographic location of the given user based upon a user device associated with the given user,

obtain a preferred store location from among a plurality of store locations based upon the current geographic location;

provide the food item purchase history and use-by dates of food ingredients as inputs to a training model and apply machine learning to generate a recommended meal kit for purchase at the preferred store location, the meal kit comprising a recipe and corresponding ones of the food ingredients portioned for the recipe, each of the food ingredients being purchasable individually in portions not corresponding to the recipe, the recommended meal kit being generated so that at least one of the corresponding food ingredients at the preferred store location is stocked at a relatively high quantity relative to other ones of the plurality of store locations and has a use-by date closer than other food ingredients relative to a current date,

generate a digital promotion for the recommended meal kit, the digital promotion having a promotional value associated therewith,

adjust the promotional value based upon the current location of the given user and the corresponding use dates so that the promotional value increases with distance of the given user from the preferred store location and so that the promotional value increases closer to the use-by dates relative to the current date, and

communicate the recommended meal kit and the digital promotion to a user device associated with the given user.

12. The meal kit recommendation server of claim 11 wherein the processor is configured to determine a meal serving size based upon quantities of a given food item from the food item purchase history, and generate the recommended meal kit to have a meal kit serving size based upon the meal serving size.

13. The meal kit recommendation server of claim 11 wherein the processor is configured to determine historical food ingredients from the food item purchase history and generate the recommended meal kit to include the food ingredients portioned for the recipe based upon the historical food ingredients.

14. The meal kit recommendation server of claim 11 wherein the processor is configured to determine at least one food type based upon the food item purchase history and generate the recommended meal kit based upon the at least one food type.

15. A method of recommending a meal kit comprising:

using a meal kit recommendation server to

obtain a food item purchase history associated with a given user,

obtain a current geographic location of the given user based upon a user device associated with the given user,

obtain a preferred store location from among a plurality of store locations based upon the current geographic location,

provide the food item purchase history and use-by dates of food ingredients as inputs to a training model and apply machine learning to generate a recommended meal kit for purchase at the preferred store location, the meal kit comprising a recipe and corresponding ones of the food ingredients portioned for the recipe, each of the food ingredients being purchasable individually in portions not corresponding to the recipe, the recommended meal kit being generated so that at least one of the corresponding food ingredients at the preferred store location is stocked at a relatively high quantity relative to other ones of the plurality of store locations and has a use-by date closer than other food ingredients relative to a current date,

generate a digital promotion for the recommended meal kit, the digital promotion having a promotional value associated therewith,

adjust the promotional value based upon the current location of the given user and the corresponding use-by dates so that the promotional value increases with distance of the given user from the preferred store location and so that the promotional value increases closer to the use-by dates relative to the current date, and

communicate the recommended meal kit and the digital promotion to a user device associated with the given user.

16. The method of claim 15 wherein using the meal kit recommendation server comprises using the meal kit recommendation server to determine a meal serving size based upon quantities of a given food item from the food item purchase history, and generate the recommended meal kit to have a meal kit serving size based upon the meal serving size.

17. The method of claim 15 wherein using the meal kit recommendation server comprises using the meal kit recommendation server to determine historical food ingredients from the food item purchase history and generate the recommended meal kit to include the food ingredients portioned for the recipe based upon the historical food ingredients.

18. The method of claim 15 wherein using the meal kit recommendation server comprises using the meal kit recommendation server to determine at least one food type based upon the food item purchase history and generate the recommended meal kit based upon the at least one food type.

19. A non-transitory computer readable medium for recommending a meal kit, the non-transitory computer readable medium comprising computer executable instructions that when executed by a processor cause the processor to perform operations comprising:

obtaining a food item purchase history associated with a given user;

obtaining a current geographic location of the given user based upon a user device associated with the given user;

obtaining a preferred store location from among a plurality of store locations based upon the current geographic location;

providing the food item purchase history and use-by dates of food ingredients as inputs to a training model and apply machine learning to generate a recommended meal kit for purchase at the preferred store location, the meal kit comprising a recipe and corresponding ones of the food ingredients portioned for the recipe, each of the food ingredients being purchasable individually in portions not corresponding to the recipe, the recommended meal kit being generated so that at least one of the corresponding food ingredients at the preferred store location is stocked at a relatively high quantity relative to other ones of the plurality of store locations and has a use-by date closer than other food ingredients relative to a current date;

generating a digital promotion for the recommended meal kit, the digital promotion having a promotional value associated therewith;

adjusting the promotional value based upon the current location of the given user and the corresponding use-by dates so that the promotional value increases with distance of the given user from the preferred store location and so that the promotional value increases closer to the use-by dates relative to the current date; and

communicating the recommended meal kit and the digital promotion to a user device associated with the given user.

20. The non-transitory computer readable medium of claim 19 wherein the operations comprise determining a meal serving size based upon quantities of a given food item from the food item purchase history, and generating the recommended meal kit to have a meal kit serving size based upon the meal serving size.

21. The non-transitory computer readable medium of claim 19 wherein the operations comprise determining historical food ingredients from the food item purchase history and generating the recommended meal kit to include the food ingredients portioned for the recipe based upon the historical food ingredients.

22. The non-transitory computer readable medium of claim 19 wherein the operations comprise determining at least one food type based upon the food item purchase history and generating the recommended meal kit based upon the at least one food type.