COMPUTER SYSTEM AND METHOD FOR ANALYZING DATA SETS AND GENERATING PERSONALIZED RECOMMENDATIONS

• US 20120095862A1
• Filed: 10/14/2011
• Published: 04/19/2012
• Est. Priority Date: 10/15/2010
• Status: Abandoned Application

• Current Assignee: OpenTable, Inc. (Booking Holdings, Inc., fka Priceline Group Inc.)
• Original Assignee: NESS Computing Incorporated (Booking Holdings, Inc., fka Priceline Group Inc.)
• Inventors: Schiff, Jeremy Ryan, Twohey, Paul Kenneth, Wu, Leejay, Reese, Corey, Schlansker, Steven Charles, Chatterji, Sourav
• Application Number:
  US13/274,226
• Publication Number:
  US 20120095862A1
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 30/0641 Shopping interfaces
• CPC Class Codes: G06Q 30/0643 Graphical representation of items or shoppers

Abstract

Embodiments of the invention relate to a computer-implemented method and system for generating personalized recommendations for a target user based at least on stored data about the target user. The method comprises obtaining, at the server computer, data from a plurality of data sources, including entity data associated with a plurality of entities, stored in an entity database, or personal data associated with a plurality of users, stored in a user database. The personalized recommendations system then merges the entity data or personal data and maps the entity or personal data to a corresponding entity or target user, respectively. The entity or personal data is differentiated, a relevance is determined, a weight is assigned to the data and corresponding source to canonicalize the data, the respective databases are updated with the corresponding data, and then a set of personalized recommendations to the target user is generated using the updated databases.

Claims

1. A computer-implemented method for generating personalized recommendations for a target user based at least on stored data about the target user, the method comprising:

obtaining, at a server computer, entity data from a plurality of data sources, wherein the entity data is associated with an entity in a plurality of entities, wherein the entity provides goods or services to the target user;

storing the entity data at the server computer, wherein the entity data associated with the plurality of entities is stored in an entity database;

merging, by the server computer, the entity data from the plurality of data sources;

mapping, by the server computer, the entity data from the plurality of data sources to a corresponding entity;

differentiating, by the server computer, the entity data from the plurality of data sources associated with the corresponding entity;

determining, by the server computer, a relevance associated with the entity data and data source, wherein the relevance is determined by a computer process to cross-reference the entity data and data source;

assigning, by the server computer, a weight to the entity data and the data source associated with the entity data based on the relevance, thereby canonicalizing the entity data;

updating the entity database with the entity data associated with the corresponding entity; and

generating, by the server computer, a set of personalized recommendations to the target user using the updated entity database.

2. The method of claim 1, further comprising:

obtaining, at the server computer, feedback data from a plurality of users, wherein the feedback data is associated to the target user in the plurality of users;

obtaining, at the server computer, location data from an external data source in a plurality of data sources associated with the target user, wherein the location data is associated with an entity in a plurality of entities;

storing the location data at the server computer, wherein location data associated with the plurality of entities is stored in the entity database;

determining, by the server computer, an entity that is associated with the feedback data from the target user;

determining, by the server computer, if the entity associated with the feedback data from the target user corresponds with an entity associated with location data in the entity database;

mapping the entity associated with the feedback data to the corresponding entity associated with the location data, wherein if the entity associated with the feedback data from the target user does not correspond with an entity associated with location data in the entity database, the entity database is updated to include the entity associated with the feedback data from the target user;

updating the entity database to include the feedback data from the target user associated the entity, with the location data associated with the corresponding entity; and

generating a set of personalized recommendations to the target user based on the updated entity database.

3. The method of claim 1, further comprising:

obtaining, at the server computer, personal data from the plurality of data sources, wherein the personal data is associated with the target user in the plurality of users;

storing the personal data at the server computer, wherein personal data associated with the plurality of users is stored in a user database;

merging, by the server computer, the personal data from the plurality of data sources;

mapping, by the server computer, the personal data from the plurality of data sources to the target user;

updating the user database with the personal data and stored data associated with the target user;

determining, at the server computer, whether an entity is associated with the personal data from the target user;

determining, at the server computer, whether the entity associated with the personal data corresponds with an entity associated with location data in the entity database;

mapping the entity associated with the personal data from the target user to the corresponding entity associated with the location data;

determining, at the server computer, inferred location data based on the mapping;

updating the entity database to include the inferred location data associated with the corresponding entity;

creating a personal profile for the target user based on the personal data;

analyzing, at the server computer, the personal data and stored data associated with the target user to determine a user-specific relevance;

updating the personal profile of the target user with the user-specific relevance; and

generating a set of personalized recommendations to the target user based on the updated personal profile of the user using the updated entity database.

4. The method of claim 2, further comprising:

determining, at the server computer, whether the feedback data relates to removing the entity and its associated location data from the location data database; and

updating the location data database to remove the entity and its associated location data.

5. The method of claim 2, wherein the location data includes geographical data identifying the location of the target user associated with the plurality of data sources.

6. The method of claim 3, further comprising:

categorizing the personal data associated with the target user into a category in a plurality of categories;

assigning a weight to the category associated with the target user based on the user-specific relevance, stored data, and personal data associated with the target user;

wherein in the plurality of categories each category has its own weight; and

updating the personal profile of the target user with the categories and their assigned weights.

7. The method of claim 6, further comprising:

merging the personal data associated with the target user with personal data associated with the plurality of users;

categorizing the personal data associated with the plurality of users into the category in a plurality of categories;

cross-referencing the personal data associated with the plurality of users with the location data associated with the plurality of entities to determine a global relevance, wherein the global relevance is associated with the plurality of users;

assigning a global weight to the category associated with the target user based on the global relevance, wherein in the plurality of categories each category has its own global weight; and

updating the personal profile of the target user with the global relevance, and global weight assigned to the category.

8. The method of claim 3, wherein the target user is a primary user, the method further comprising:

analyzing, at the server computer, the personal profile of the primary user and personal profiles of at least one or more secondary users;

cross-referencing, at the server computer, the personal profile of the primary user, with the personal profiles of the at least one or more secondary users;

determining a relationship between the primary user and the at least one or more secondary users;

determining a group relevance based on the relationship between the primary user and the at least one or more secondary users; and

updating the personal profile of the primary user and the personal profiles of the at least one or more secondary users with the relationship and the group relevance.

9. The method of claim 8, further comprising:

assigning a group weight to the category associated with the primary user based on the group relevance, wherein in the plurality of categories each category has its own group weight; and

updating the personal profiles of the primary user and the personal profiles of the at least one or more secondary users with the group weight.

10. The method of claim 9, further comprising generating a set of group personalized recommendations for the primary user and the at least one or more secondary users.

11. The method of claim 9, further comprising generating a set of personalized recommendations for the primary user based on an updated profile of a secondary user.

23. The method of claim 3, further comprising:

determining a location of the target user based on the location data and personal data from the plurality of data sources associated with the target user; and

alerting the target user with a personalized recommendation from the set of personalized recommendations, wherein the personalized recommendation includes an entity, wherein the entity is within close proximity of the location of the target user.

25. The method of claim 8, the method further comprising:

analyzing the location data associated with the target user, and the location data associated with the at least one or more secondary users;

determining whether the target user and the at least one or more secondary users are in the same location; and

updating the personal profile of the target user and the personal profiles of the at least one or more secondary users with the analysis of the same location data.

12. A system for generating personalized recommendations for a target user based at least on stored data about the target user, the system comprising a server computer comprising a processor and a computer-readable medium, the computer-readable medium comprising code executable by the processor to perform a method, the method comprising:

obtaining, at the server computer, entity data from a plurality of data sources, wherein the entity data is associated with an entity in a plurality of entities, wherein the entity provides goods or services to the target user;

storing the entity data at the server computer, wherein the entity data associated with the plurality of entities is stored in an entity database;

merging, by the server computer, the entity data from the plurality of data sources;

mapping, by the server computer, the entity data from the plurality of data sources to a corresponding entity;

differentiating, by the server computer, the entity data from the plurality of data sources associated with the corresponding entity;

determining, by the server computer, a relevance associated with the entity data and data source, wherein the relevance is determined by a computer process to cross-reference the entity data and data source;

assigning, by the server computer, a weight to the entity data and the data source associated with the entity data based on the relevance, thereby canonicalizing the entity data;

updating the entity database with the entity data associated with the corresponding entity; and

generating, by the server computer, personalized recommendations to the target user using the updated entity database.

13. The system of claim 12, the method further comprising:

obtaining, at the server computer, feedback data from a plurality of users, wherein the feedback data is associated to the target user in the plurality of users;

obtaining, at the server computer, location data from an external data source in a plurality of data sources associated with the target user, wherein the location data is associated with an entity in a plurality of entities;

storing the location data at the server computer, wherein location data associated with the plurality of entities is stored in the entity database;

determining, by the server computer, an entity that is associated with the feedback data from the target user;

determining, by the server computer, if the entity associated with the feedback data from the target user corresponds with an entity associated with location data in the entity database;

mapping the entity associated with the feedback data to the corresponding entity associated with the location data, wherein if the entity associated with the feedback data from the target user does not correspond with an entity associated with location data in the entity database, the entity database is updated to include the entity associated with the feedback data from the target user;

updating the entity database to include the feedback data from the target user associated the entity, with the location data associated with the corresponding entity; and

generating a set of personalized recommendations to the target user based on the updated entity database.

14. The system of claim 12, the method further comprising:

obtaining, at the server computer, personal data from the plurality of data sources, wherein the personal data is associated with the target user in the plurality of users;

storing the personal data at the server computer, wherein personal data associated with the plurality of users is stored in a user database;

merging, by the server computer, the personal data from the plurality of data sources;

mapping, by the server computer, the personal data from the plurality of data sources to the target user;

updating the user database with the personal data and stored data associated with the target user;

determining, at the server computer, whether an entity is associated with the personal data from the target user;

determining, at the server computer, whether the entity associated with the personal data corresponds with an entity associated with location data in the entity database;

mapping the entity associated with the personal data from the target user to the corresponding entity associated with the location data;

determining, at the server computer, inferred location data based on the mapping;

updating the entity database to include the inferred location data associated with the corresponding entity;

creating a personal profile for the target user based on the personal data;

analyzing, at the server computer, the personal data and stored data associated with the target user to determine a user-specific relevance;

updating the personal profile of the target user with the user-specific relevance; and

generating a set of personalized recommendations to the target user based on the updated personal profile of the user using the updated entity database.

15. The system of claim 13, the method further comprising:

determining, at the server computer, whether the feedback data relates to removing the entity and its associated location data from the location data database; and

updating the location data database to remove the entity and its associated location data.

16. The system of claim 13, wherein the location data includes geographical data identifying the location of the target user associated with the plurality of data sources.

17. The system of claim 14, the method further comprising:

categorizing the personal data associated with the target user into a category in a plurality of categories;

assigning a weight to the category associated with the target user based on the user-specific relevance, stored data, and personal data associated with the target user;

wherein in the plurality of categories each category has its own weight; and

updating the personal profile of the target user with the categories and their assigned weights.

18. The system of claim 17, further comprising:

merging the personal data associated with the target user with personal data associated with the plurality of users;

categorizing the personal data associated with the plurality of users into the category in a plurality of categories;

cross-referencing the personal data associated with the plurality of users with the location data associated with the plurality of entities to determine a global relevance, wherein the global relevance is associated with the plurality of users;

assigning a global weight to the category associated with the target user based on the global relevance, wherein in the plurality of categories each category has its own global weight; and

updating the personal profile of the target user with the global relevance, and global weight assigned to the category.

19. The method of claim 14, wherein the target user is a primary user, the method further comprising:

analyzing, at the server computer, the personal profile of the primary user and personal profiles of at least one or more secondary users;

cross-referencing, at the server computer, the personal profile of the primary user, with the personal profiles of the at least one or more secondary users;

determining a relationship between the primary user and the at least one or more secondary users;

determining a group relevance based on the relationship between the primary user and the at least one or more secondary users; and

updating the personal profile of the primary user and the personal profiles of the at least one or more secondary users with the relationship and the group relevance.

20. The method of claim 19, further comprising:

assigning a group weight to the category associated with the primary user based on the group relevance, wherein in the plurality of categories each category has its own group weight; and

updating the personal profiles of the primary user and the personal profiles of the at least one or more secondary users with the group weight.

21. The method of claim 20, further comprising generating a set of group personalized recommendations for the primary user and the at least one or more secondary users.

22. The method of claim 20, further comprising generating a set of personalized recommendations for the primary user based on an updated profile of a secondary user.

24. The system of claim 14, the method further comprising:

determining a location of the target user based on the location data and personal data from the plurality of data sources associated with the target user; and

alerting the target user with a personalized recommendation from the set of personalized recommendations, wherein the personalized recommendation includes an entity, wherein the entity is within close proximity of the location of the target user.

26. The system of claim 19, the method further comprising:

analyzing the location data associated with the target user, and the location data associated with the at least one or more secondary users;

determining whether the target user and the at least one or more secondary users are in the same location; and

updating the personal profile of the target user and the personal profiles of the at least one or more secondary users with the analysis of the same location data.