Systems and methods for enhancing shopping experience in physical stores

1. A system for presenting personalized shopping related recommendations to one or more users, the shopping recommendations related to one or more physical stores, the system comprising:

a processor; and

a memory coupled to the processor for executing a plurality of modules present in the memory, the plurality of modules comprising:

a receiving module configured to receive:

one or more items to buy and at least one constraint associated with the user, the at least one constraint being specified by the user in real-time;

at least one persona associated with the user; and

at least one activity associated with the user in one or more physical stores, the at least one activity being tracked using one or more smart devices, wherein the one or more smart devices comprise at least one of:

a sensor installed in the one or more physical stores, and

a combination of a smartphone, comprising a camera, and a wearable device;

a persona learning module configured to:

generate the at least one persona based on at least one of: ethnographic data obtained from the user, demographic data associated with the user, buying behavioral data associated with the user, and social networking data associated with the user;

classify the user into a pre-defined personal bucket; and

represent the at least one persona in a form of a matrix representation;

a recommendation engine configured to:

process the at least one constraint, the at least one persona, and the at least one activity, and to generate a selectable ranked set of the personalized recommendations for the user; and

a user interface unit configured to:

generate the selectable ranked set of the personalized recommendations to be displayed to the user for a subsequent action.

2. The system of claim 1 , wherein the personalized recommendations comprise at least one of: the one or more physical stores, a route to the one or more physical stores, details of the one or more physical stores for shopping, and at least an item to be shopped in the one or more physical stores.

3. The system of claim 1 , wherein the persona learning module further comprises:

an evolving persona module configured to update the matrix representation associated with the user based on change in at least one of: the ethnographic data obtained from the user, the demographic data associated with the user, the buying behavioral data associated with the user, the social networking data associated with the user, and at least one recommendation selected by the user from the personalized recommendations.

4. The system of claim 1 , wherein the user specified constraint comprises at least one of: a time constraint, a budget constraint, a distance constraint, a one more items to buy constraint, and a location constraint.

5. The system of claim 1 , wherein the user provides feedback on the personalized recommendations.

6. The system of claim 1 , wherein the receiving module is configured to receive a query from the user, the query including details of one or more items to be purchased by the user.

7. An apparatus comprising:

one or more non-transitory computer-readable media storing a persona learning unit, a data collection unit, and a recommendation engine;

the persona learning unit being configured to:

receive one or more of ethnographic data related to a user, demographic data associated with the user, behavioral data associated with the user, and social networking data associated with the user;

generate one or more personas of the user based on one or more of: ethnographic data related to the user, the demographic data associated with the user, the behavioral data associated with the user, and the social networking data associated with the user; and

classify the user into a predefined personal bucket and represent the persona in a form of a matrix representation,

the data collection unit being configured to obtain the one or more personas associated with the user, the one or more constraints associated with the user, and activity data of the user, the activity data being sensed using one or more smart devices, wherein the one or more smart devices comprise at least one of:

a sensor installed in one or more physical stores, and

a combination of a smartphone, comprising a camera, a wearable device; and

the recommendation engine being configured to:

process the one or more personas of the user, the one or more constraints associated with the user, and the activity data of the user, to generate a pre-defined number of personalized recommendations for the user; and

send a notification of the personalized recommendations to the user.

8. The apparatus of claim 7 , wherein the personalized recommendations comprise one or more of: a physical store for shopping, a route of the store, details of a physical store for shopping, and one or more items to be shopped.

9. The apparatus of claim 7 , wherein the data collection unit is further configured to: update the matrix representation associated with the user based on change in one or more of: the ethnographic data related to the user, the demographic data associated with the user, the buying behavioral data associated with the user, the social networking data associated with the user, and a recommendation selected by the user from the personalized recommendations.

10. The apparatus of claim 7 , wherein the one or more constraints associated with the user comprise: a time constraint, a budget constraint, a distance constraint, one or more items to buy constraint, and a location constraint.

11. The apparatus of claim 7 , wherein the recommendation engine is configured to receive feedback from the user about the personalized recommendations.

12. A method for assisting users by presenting recommendations for shopping at physical stores, the method comprising:

receiving at least one persona associated with a user based on one or more of: ethnographic data obtained from the user, demographic data associated with the user, buying behavioral data associated with the user, social networking data associated with the user, and one or more historical activities of the user inside one or more physical stores, wherein the one or more historical activities are sensed using at least one of:

a sensor installed in the one or more physical stores, and

a combination of a smartphone, comprising a camera, and a wearable device;

receiving one or more constraints associated with the user, the constraints being specified by the user and including one or more of: a time constraint, a budget constraint, a distance constraint, an item to buy constraint, and a location constraint;

analyzing the at least one persona, the one or more constraints, and the one or more historical activities, to generate a pre-defined number of personalized recommendations; and

generating a graphical user interface for displaying the personalized recommendations to the user.

13. The method of claim 12 further comprising:

generating the at least one persona based on the ethnographic data obtained from a user, the demographic data associated with the user, the buying behavioral data associated with the user, and the social networking data associated with the user, and the one or more historical activities of the user inside the one or more physical stores;

classifying the user into the persona selected from at least one pre-defined category of persona buckets and representing the persona in a form of a matrix representation; and

updating the matrix representation associated with the user based on change in at least one of: the ethnographic data related to the user, the demographic data associated with the user, the buying behavioral data associated with the user, the social networking data associated with the user, and a recommendation selected by the user from the personalized recommendations.

14. The method of claim 12 further comprising receiving a query from the user.

15. The method of claim 12 further comprising transmitting the personalized recommendations to the user.

16. The method of claim 12 , wherein the personalized recommendations are selectable by the user.