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

• US 20120095863A1
• Filed: 10/14/2011
• Published: 04/19/2012
• Est. Priority Date: 10/15/2010
• Status: Active Grant

• 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, Reese, Corey, Wang, Yige, Goodson, Scott, Twohey, Paul Kenneth
• Granted Patent: US 9,396,492 B2
• 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

Abstract

Embodiments of the invention relate to a computer-implemented method and system for providing personalized recommendations for a target user based at least on stored data about the target user. The method comprises obtaining a plurality of feedback data from a plurality of users, wherein the feedback data comprises an indication of a media object, a response obtained from target user related to the feedback data, and at least one demographic data element associated with the target user. A set of personalized recommendations for the target user are identified based at least on stored data about the target user and the feedback data related to the user. The personalized recommendations system identifies media objects to potentially provide to the target user, and selects or filters the identified media objects to form a set of personalized media objects associated with the set of personalized recommendations.

Claims

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

obtaining, at a server computer, a plurality of feedback data from a plurality of users, wherein feedback data comprises an indication of a media object, a response obtained from the target user related to the feedback data, and at least one demographic data element associated with the target user providing the feedback data;

storing the plurality of feedback data in a feedback database, wherein the feedback database is searchable by media objects;

identifying, by the server computer, a set of personalized recommendations for the target user, based at least on stored data about the target user and the feedback data related to the target user;

identifying, by the server computer, media objects to potentially provide to the target user in connection with the set of personalized recommendations;

selecting or filtering the identified media objects to form a set of personalized media objects that are to be provided to the target user, wherein the selecting or filtering is determined by a computer process that considers which media objects are associated with desired responses of users having demographic data elements aligned with demographic data about the target user; and

providing the set of personalized recommendations, including the set of media objects to the target user.

2. The method of claim 1, wherein the set of personalized recommendations are for entities, comprising venues, merchants, media, products, and/or service providers.

3. The method of claim 1, wherein a desired response of the target user is an indication of relevance or interest in a recommendation in the set of personalized recommendations, wherein each recommendation in the set of personalized recommendations corresponds to a media object in the set of personalized media objects.

4. The method of claim 3, wherein the indication of relevance or interest is a click-through of the media object.

5. The method of claim 3, wherein the indication of relevance or interest is a purchase, a visit to a website related to an entity, a visit to the entity, a transaction, a viewing time, or response to the personalized media object related to the recommendation.

6. The method of claim 1, wherein at least some of the feedback data have an associated location, where the associated location represents an entity that provides a venue, goods, and/or services for which the feedback item pertains, the method comprising:

determining, from raw feedback data used to generate the feedback item, a feedback message;

identifying at least a suggested location from the raw feedback data;

mapping the suggested location to a known, enumerated location associated with an entity in an entity database, thereby canonicalizing locations and canonicalizing entities associated with feedback data; and

using a canonicalized location or canonicalized entity when selecting or filtering media objects to potentially provide to the target user.

7. The method of claim 1, wherein the media object includes an image, text, audio, or video.

8. The method of claim 1, further comprising repeatedly obtaining feedback data from the target user to iteratively identify the set of personalized recommendations and the set of personalized media objects for the target user.

9. The method of claim 6, further comprising:

creating a personal profile for the target user based on at least stored data about the target user and feedback data related to the target user;

identifying, by the server computer, a sentiment response to the canonicalized location or canonicalized entity from the feedback data, wherein the identifying is determined by a computer process that interprets the feedback message;

updating the personal profile of the target user based on the sentiment response;

updating the entity database based on the sentiment response; and

using the updated personal profile when selecting or filtering media objects to potentially provide to the target user.

10. The method of claim 9, 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 a personal profile 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;

identifying, by the server computer, a set of group personalized recommendations for the primary user and the at least one or more secondary users, based at least on stored data about the primary user, stored data about the at least one or more secondary users, the feedback data related to the primary user, and the feedback data related to the at least one or more secondary users;

identifying, by the server computer, group media objects to potentially provide to the primary user in connection with the set of group personalized recommendations;

selecting or filtering the identified media objects to form a set of group personalized media objects that are to be provided to the primary user, wherein the selecting or filtering is determined by a computer process that considers which media objects are associated with desired responses of users having demographic data elements aligned with demographic data about the primary user and the at least one or more secondary users; and

providing the set of group personalized recommendations, including the set of group personalized media objects to the primary user and the at least one or more secondary users.

11. The method of claim 10, wherein the set of group personalized media objects and the set of group personalized recommendations for the primary user are based on the updated profile of a secondary user.

12. The method of claim 10, further comprising providing a set of personalized summaries to the target user, related to the set of personalized media objects and the set of personalized recommendations, wherein the set of personalized summaries comprises summary content related to the personalized recommendation.

13. The method of claim 12, wherein the summary content is determined by a computer process that considers what summary content is associated with desired responses of users having demographic data elements aligned with demographic data elements about the target user, and includes feedback data from the target user.

14. The method of claim 13, wherein the set of personalized summaries comprises a set of personalized explanations to the target user, to inform why the set of recommendations are personalized to the target user.

15. The method of claim 13, wherein the summary content comprises feedback data from the at least one or more secondary users associated with the primary user.

16. The method of claim 10, wherein the relationship between the primary user and the at least one or more secondary users is determined by a computer process that considers similarities in the feedback message of the primary user and the feedback messages of the at least one or more secondary users.

17. The method of claim 14, wherein the set of personalized explanations to the target user is based on the canonicalized location or canonicalized entity from the entity database.

18. The method of claim 8, further comprising:

using the feedback data previously obtained and stored in the feedback database to create a feedback history of the target user;

analyzing the feedback history of the target user;

creating a feedback filter based on the feedback history; and

applying the feedback filter to select or filter the set of personalized recommendations and the set of personalized media objects.

19. The method of claim 10, further comprising:

analyzing the feedback data related to the primary user and the feedback data related to the at least one or more secondary users;

identifying the associated location of the primary user and the associated locations of the at least one or more secondary users based on the feedback data related to the primary user and the feedback data related to the at least one or more secondary users, respectively; and

updating the relationship between the primary user and the at least one or more secondary users, wherein the relationship between the primary user and the at least one or more secondary users is updated by a computer process that determines whether the associated location of the primary user is in close proximity of the associated location of the at least one or more secondary users.

20. The method of claim 7, wherein the media objects are arranged in a personalized layout, wherein the personalized layout is determined by a computer process that considers which layout of the media objects are associated with desired responses of users having demographic data elements aligned with demographic data elements about the target user.

26. The system of claim 1, wherein at least some of the feedback data have an associated location, where the associated location represents an entity that provides a venue, goods, and/or services for which the feedback item pertains, the method further comprising:

determining, from raw feedback data used to generate the feedback item, a feedback message;

identifying at least a suggested location from the raw feedback data;

mapping the suggested location to a known, enumerated location associated with an entity in an entity database, thereby canonicalizing locations and canonicalizing entities associated with feedback data; and

using a canonicalized location or canonicalized entity when selecting or filtering media objects to potentially provide to the target user.

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

creating a personal profile for the target user based on at least stored data about the target user and feedback data related to the target user;

identifying, by the server computer, a sentiment response to the canonicalized location or canonicalized entity from the feedback data, wherein the identifying is determined by a computer process that interprets the feedback message;

updating the personal profile of the target user based on the sentiment response;

updating the entity database based on the sentiment response; and

using the updated personal profile when selecting or filtering media objects to potentially provide to the target user.

30. The system of claim 29, 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 a personal profile 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;

identifying, by the server computer, a set of group personalized recommendations for the primary user and the at least one or more secondary users, based at least on stored data about the primary user, stored data about the at least one or more secondary users, the feedback data related to the primary user, and the feedback data related to the at least one or more secondary users;

identifying, by the server computer, group media objects to potentially provide to the primary user in connection with the set of group personalized recommendations;

selecting or filtering the identified media objects to form a set of group personalized media objects that are to be provided to the primary user, wherein the selecting or filtering is determined by a computer process that considers which media objects are associated with desired responses of users having demographic data elements aligned with demographic data about the primary user and the at least one or more secondary users; and

providing the set of group personalized recommendations, including the set of group personalized media objects to the primary user and the at least one or more secondary users.

31. The system of claim 30, wherein the set of group personalized media objects and the set of group personalized recommendations for the primary user are based on the updated profile of a secondary user.

32. The system of claim 30, the method further comprising providing a set of personalized summaries to the target user, related to the set of personalized media objects and the set of personalized recommendations, wherein the set of personalized summaries comprises summary content related to the personalized recommendation.

33. The system of claim 32, wherein the summary content is determined by a computer process that considers what summary content is associated with desired responses of users having demographic data elements aligned with demographic data elements about the target user, and includes feedback data from the target user.

34. The system of claim 33, wherein the set of personalized summaries comprises a set of personalized explanations to the target user, to inform why the set of recommendations are personalized to the target user.

35. The system of claim 33, wherein the summary content comprises feedback data from the at least one or more secondary users associated with the primary user.

36. The system of claim 30, wherein the relationship between the primary user and the at least one or more secondary users is determined by a computer process that considers similarities in the feedback message of the primary user and the feedback messages of the at least one or more secondary users.

37. The system of claim 34, wherein the set of personalized explanations to the target user is based on the canonicalized location or canonicalized entity from the entity database.

39. The system of claim 30, the method further comprising:

analyzing the feedback data related to the primary user and the feedback data related to the at least one or more secondary users;

identifying the associated location of the primary user and the associated locations of the at least one or more secondary users based on the feedback data related to the primary user and the feedback data related to the at least one or more secondary users, respectively; and

updating the relationship between the primary user and the at least one or more secondary users, wherein the relationship between the primary user and the at least one or more secondary users is updated by a computer process that determines whether the associated location of the primary user is in close proximity of the associated location of the at least one or more secondary users.

41. The method of claim 20, wherein the personalized layout is displayed on a device, wherein the device comprises a touchscreen.

42. The method of claim 41, further comprising shaking the device displaying the personalized layout of the set of personalized media objects associated with the set personalized recommendations refreshes the set of personalized recommendations.

43. The method of claim 41, further comprising lobbing the device toward another user passes a personalized recommendation for the target user to the other user.

44. The method of claim 41, further comprising blowing on the device removes the set of personalized recommendations.

45. The method of claim 41, further comprising pressing and holding down on a media object displayed on the device reveals more information related to a recommendation associated with the media object.

51. The method of claim 41, further comprising swiping a personalized media object associated with a personalized recommendation towards another user to pass the personalized recommendation for the target user to the other user.

52. The method of claim 41, further comprising pulling two fingers together on the touchscreen to simulate crinkling a media object associated with a personalized recommendation to remove the personalized recommendation.

21. A system for providing personalized recommendations for a target user based at least on stored data about the target user, the system comprising a server computer comprising a process 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, a plurality of feedback data from a plurality of users, wherein feedback data comprises an indication of a media object, a response obtained from the target user related to the feedback data, and at least one demographic data element associated with the target user providing the feedback data;

storing the plurality of feedback data in a feedback database, wherein the feedback database is searchable by media objects;

identifying, by the server computer, a set of personalized recommendations for the target user, based at least on stored data about the target user and the feedback data related to the target user;

identifying, by the server computer, media objects to potentially provide to the target user in connection with the set of personalized recommendations;

selecting or filtering the identified media objects to form a set of personalized media objects that are to be provided to the target user, wherein the selecting or filtering is determined by a computer process that considers which media objects are associated with desired responses of users having demographic data elements aligned with demographic data about the target user; and

providing the set of personalized recommendations, including the set of media objects to the target user.

22. The system of claim 21, wherein the set of personalized recommendations are for entities, comprising venues, merchants, media, products, and/or service providers.

23. The system of claim 21, wherein a desired response of the target user is an indication of relevance or interest in a recommendation in the set of personalized recommendations, wherein each recommendation in the set of personalized recommendations corresponds to a media object in the set of personalized media objects.

24. The system of claim 23, wherein the indication of relevance or interest is a click-through of the media object.

25. The system of claim 23, wherein the indication of relevance or interest is a purchase, a visit to a website related to an entity, a visit to the entity, a transaction, a viewing time, or response to the personalized media object related to the recommendation.

27. The system of claim 21, wherein the media object includes an image, audio, or video.

28. The system of claim 21, the method further comprising repeatedly obtaining feedback data from the target user to iteratively identify the set of personalized recommendations and the set of personalized media objects for the target user.

38. The system of claim 28, the method further comprising:

using the feedback data previously obtained and stored in the feedback database to create a feedback history of the target user;

analyzing the feedback history of the target user;

creating a feedback filter based on the feedback history; and

applying the feedback filter to select or filter the set of personalized recommendations and the set of personalized media objects.

40. The system of claim 27, wherein the media objects are arranged in a personalized layout, wherein the personalized layout is determined by a computer process that considers which layout of the media objects are associated with desired responses of users having demographic data elements aligned with demographic data elements about the target user.

46. The system of claim 40, further comprising a device displaying the personalized layout, wherein the device comprises a touchscreen.

47. The system of claim 46, wherein shaking the device displaying the personalized layout of the set of personalized media objects associated with the set personalized recommendations refreshes the set of personalized recommendations.

48. The system of claim 46, wherein lobbing the device toward another user passes a personalized recommendation for the target user to the other user.

49. The system of claim 46, wherein blowing on the device removes the set of personalized recommendations.

50. The system of claim 46, wherein pressing and holding down on a media object displayed on the device reveals more information related to a recommendation associated with the media object.

53. The system of claim 46, wherein pulling two fingers apart on the touchscreen to simulate tearing a media object associated with a personalized recommendation to remove the personalized recommendation.

54. The system of claim 46, wherein swiping a personalized media object associated with a personalized recommendation towards another user to pass the personalized recommendation for the target user to the other user.

55. The system of claim 46, wherein pulling two fingers together on the touchscreen to simulate crinkling a media object associated with a personalized recommendation to remove the personalized recommendation.

56. The system of claim 46, wherein pulling two fingers apart on the touchscreen to simulate tearing a media object associated with a personalized recommendation to remove the personalized recommendation.