System architecture for customer genome construction and analysis

1. A method for generating a robust recipient profile and identifying physical items from location-based updates, comprising:

receiving, by a processor, data from a plurality of data channels, where the received data comprises batch data and stream data;

processing, by the processor, the received data to generate the robust recipient profile by:

matching the received data to a recipient;

assigning a recipient identifier to the received data, the recipient identifier corresponding to the matched recipient; and

analyzing a selected portion of the received data to determine one or more attributes of the recipient;

performing, by the processor, reconciliation of received data from multiple data sources by:

determining whether one or more data objects in the received data contain information regarding a common attribute of the recipient;

determining a level of trust of the one or more data objects based on the data source of the data object; and

adjusting the common attribute of the recipient based on the determined level of trust of the one or more data objects;

ranking, by the processor, the determined attributes of the recipient;

determining, by the processor, which attributes are tagged to the recipient and assigning corresponding tags to the recipient;

monitoring, by the processor, the plurality of data channels for additional data comprising real-time location information originated by a mobile device associated with the recipient identifier of the recipient;

flagging, by the processor, the additional data in response to determination that the mobile device is stationary for a predetermined threshold time;

determining, by the processor, if a real-time update flag is present;

updating, by the processor, the robust recipient profile in real-time based on a determination that a real-time update flag is present;

updating, by the processor, the robust recipient profile through a batch update based on a determination that a real-time update flag is not present;

selecting, by the processor, item metadata in response to the location of the mobile device being proximate to a physical item associated with the item metadata and the item metadata being associated with a tag that matches at least one of the corresponding tags of the customer;

generating, by the processor, based on the selected item metadata, a recipient-specific custom metadata recommendation based on the robust recipient profile; and

transmitting, by the processor, in response to the real-time update flag being present and in response to the location of the mobile device being proximate to a physical item associated with the item metadata, an instruction to display the recipient-specific custom metadata recommendation on the mobile device of the recipient.

2. The method of claim 1 , where the received data comprises transactional data and product data comprising different product attributes and analyzing the selected portion of the received data to determine one or more attributes of the recipient further comprises:

combining the product data and transactional data to form a dataspace;

calculating a term frequency score for each of the product attributes for the recipient;

calculating an inverse document frequency for each of the product attributes for the recipient;

combining the term frequency and inverse document frequency to form an attribute of the recipient.

3. The method of claim 1 , where processing the received data to create the robust recipient profile further comprises:

cross-referencing the selected portion of the received data with pre-analyzed data.

4. The method of claim 1 , wherein the robust recipient profile is updated periodically, on-demand, in response to a trigger, or a combination thereof.

5. The method of claim 1 , wherein determining a recipient-specific custom metadata recommendation further comprises:

retrieving metadata and an attribute configuration file from a metadata database, the metadata comprising a set of generalized metadata in the metadata database;

matching the determined attributes of the customer with the metadata based on the attribute configuration file to determine relevant recipient-specific custom metadata;

assigning a fit factor to the relevant recipient-specific custom metadata based on a level of correlation between the relevant recipient-specific custom metadata and the determined attributes of the recipient;

categorizing the relevant recipient-specific custom metadata based on the assigned fit factor; and

ranking the relevant recipient-specific custom metadata based on a significance of the relevant recipient-specific custom metadata to a business.

6. The method of claim 5 , where categorizing the relevant recipient-specific custom metadata further comprises:

determining whether particular metadata in the metadata comprises a contextual parameter;

determining whether the particular metadata is valid by assessing whether the contextual parameter has been satisfied; and

categorizing the particular metadata as contextual metadata based on a determination that the particular metadata is valid.

7. The method of claim 5 , where categorizing the relevant recipient-specific custom metadata further comprises:

identifying regular recipient-specific custom metadata and extended recipient-specific custom metadata, where regular recipient-specific custom metadata meets or exceeds a first predetermined fit factor threshold, and where extended recipient-specific custom metadata falls below the first predetermined fit factor threshold and exceeds a second predetermined fit factor threshold.

8. The method of claim 5 , further comprising:

deriving a transaction mapping based on the robust recipient profile, the transaction mapping having one or more nodes and edges, where each node in the transaction mapping corresponds to particular metadata in the metadata, and where each edge in the transaction mapping has a weight that reflects the conditional probability of customer acceptance of a particular metadata corresponding to a second node based on customer acceptance of a particular metadata corresponding to a first node;

matching the determined attributes of the recipient against the one or more nodes, where a matching node indicates recipient acceptance of the particular metadata corresponding to the matching node; and

categorizing one or more of the relevant recipient-specific custom metadata as an extended recipient-specific custom metadata by:

identifying nodes that have a common edge with the matching node; and

determining if the weight of the common edge exceeds a predetermined probability threshold.

9. The method of claim 1 , where the received data comprises traditional data, alternate data, or a combination thereof.

10. The method of claim 9 , where the traditional data comprises product catalog data and transactional data, the product catalog data comprising a product category having a plurality of product attributes, and the transactional data comprising historical purchase information of a recipient.

11. A system comprising circuitry operable to:

receive data from a plurality of data channels, where the received data comprises batch data and stream data;

process the received data to generate a robust recipient profile, where the circuitry is operable to:

match the received data to a recipient;

assign a recipient identifier to the received data, the recipient identifier corresponding to the matched recipient; and

analyze the selected portion of the received data to determine one or more attributes of the recipient;

perform reconciliation of received data from multiple data sources, where the circuitry is operable to:

determine whether one or more data objects in the received data contain information regarding a common attribute of the recipient;

determine a level of trust of the one or more data objects based on the data source of the data object; and

adjust the common attribute of the recipient based on the determined level of trust of the one or more data objects;

rank the determined attributes of the recipient;

determine which attributes are tagged to the recipient and assign corresponding tags to the recipient;

monitor the plurality of data channels for additional data comprising real-time location information originated by a mobile device associated with the recipient identifier of the recipient;

flag the additional data in response to determination that the mobile device is stationary for a predetermined threshold time;

determine if a real-time update flag is present;

update the robust recipient profile in real-time based on a determination that a real-time update flag is present;

update the robust recipient profile through a batch update based on a determination that a real-time update flag is not present;

select by the processor, item metadata in response to the location of the mobile device being proximate to a physical item associated with the item metadata and the item metadata being associated with a tag that matches at least one of the corresponding tags of the customer;

generate, by the processor, based on the selected item metadata, a recipient-specific custom metadata recommendation based on the robust recipient profile; and

transmit, by the processor, in response to the real-time update flag being present and in response to the location of the mobile device being proximate to a physical item associated with the item metadata, an instruction to display the the recipient-specific custom metadata recommendation on the mobile device of the recipient.

12. The system of claim 11 , where the received data comprises transactional data and product data comprising different product attributes and the circuitry is operable to analyze the selected portion of the received data to determine one or more attributes of the recipient further by:

combining the product data and transactional data to form a dataspace;

calculating a term frequency score for each of the product attributes for the recipient;

calculating an inverse document frequency for each of the product attributes for the recipient;

combining the term frequency and inverse document frequency to form an attribute of the recipient.

13. The system of claim 11 , where the circuitry is further operable to:

cross-reference a selected portion of the received data with pre-analyzed data.

14. The system of claim 11 , where the circuitry is further operable to:

update the robust recipient profile periodically, on-demand, in response to a trigger, or a combination thereof.

15. The system of claim 11 , wherein to determine the recipient-specific custom metadata recommendation based on the robust recipient profile, the circuitry is further operable to:

retrieve metadata and an attribute configuration file from a metadata database, the metadata comprising a set of generalized metadata in the metadata database;

match the determined attributes of the customer with the metadata based on the attribute configuration file to determine relevant recipient-specific custom metadata;

assign a fit factor to the relevant recipient-specific custom metadata based on a level of correlation between the relevant recipient-specific custom metadata and the determined attributes of the recipient;

categorize the relevant recipient-specific custom metadata based on the assigned fit factor; and

rank the relevant recipient-specific custom metadata based on a significance of the relevant recipient-specific custom metadata to a business.

16. The system of claim 15 , where the circuitry is further operable to:

determine whether particular metadata in the metadata comprises a contextual parameter;

determine whether the particular metadata is valid by assessing whether the contextual parameter has been satisfied; and

categorize the particular metadata as contextual metadata based on a determination that the particular metadata is valid.

17. The system of claim 15 , where the circuitry is further operable to:

identify regular recipient-specific custom metadata and extended recipient-specific custom metadata, where regular recipient-specific custom metadata meets or exceeds a first predetermined fit factor threshold, and where extended recipient-specific custom metadata falls below the first predetermined fit factor threshold and exceeds a second predetermined fit factor threshold.

18. The system of claim 17 , where the traditional data comprises product catalog data and transactional data, the product catalog data comprising a product category having a plurality of product attributes, and the transactional data comprising historical purchase information of a recipient.

19. The system of claim 15 , where the circuitry is further operable to:

derive a transaction mapping based on the robust recipient profile, the transaction mapping having one or more nodes and edges, where each node in the transaction mapping corresponds to particular metadata in the metadata, and where each edge in the transaction mapping has a weight that reflects the conditional probability of customer acceptance of a particular metadata corresponding to a second node based on customer acceptance of a particular metadata corresponding to a first node;

match the determined attributes of the recipient against the one or more nodes, where a matching node indicates recipient acceptance of the particular metadata corresponding to the matching node; and

categorize one or more of the relevant recipient-specific custom metadata as an extended recipient-specific custom metadata, wherein the circuitry is operable to:

identify nodes that have a common edge with the matching node; and

determine if the weight of the common edge exceeds a predetermined probability threshold.

20. The system of claim 11 , where the received data comprises traditional data, alternate data, or a combination thereof.