GEOGRAPHICALLY LOCALIZED RECOMMENDATIONS IN A COMPUTING ADVICE FACILITY

• US 20110307478A1
• Filed: 06/08/2011
• Published: 12/15/2011
• Est. Priority Date: 11/02/2007
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Pinckney, Thomas, Dixon, Christopher, Gattis, Matthew Ryan
• Granted Patent: US 9,159,034 B2
• Time in Patent Office: Days
• US Class Current: 707/724
• CPC Class Codes: G06F 16/583 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G09B 7/04 characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation

Abstract

The present disclosure provides a geographically localized recommendation to a user through a computer-based advice facility, comprising collecting a recommendation from an Internet source, wherein the recommendation is determined to have an interestingness aspect and a geographic location aspect, comparing the collected recommendation to a derived user taste and the user'"'"'s current geographic location, determining at least one recommendation for the user based on processing on the comparison, and delivering at least one recommendation to a user'"'"'s mobile communications device, wherein the user is enabled to at least one of view, save, and share the recommendation via an application at least in part resident on the computer-based advice facility.

Claims

1. A method of providing a geographically localized recommendation to a user through a computer-based advice facility, comprising:

collecting a recommendation from an Internet source, wherein the recommendation is determined to have an interestingness aspect and a geographic location aspect;

comparing the collected recommendation to a derived user taste and the user'"'"'s current geographic location;

determining at least one recommendation for the user based on processing on the comparison; and

delivering at least one recommendation to a user'"'"'s mobile communications device, wherein the user is enabled to at least one of view, save, and share the recommendation via an application at least in part resident on the computer-based advice facility.

2. The method of claim 1, wherein the computer-based advice facility is a mobile communications device.

3. The method of claim 2, wherein the mobile communications device is a smart-phone.

4. The method of claim 1, wherein the viewing includes providing source information from the Internet source.

5. The method of claim 4, wherein the source information includes the original recommendation.

6. The method of claim 4, wherein the source information includes a rating.

7. The method of claim 4, wherein the source information includes an image associated with the Internet source.

8. The method of claim 7, wherein the image is a photo of an individual who provided the recommendation.

9. The method of claim 4, wherein the source information includes a visual indicator of an extent of similarity to the tastes of at least one individual who made the recommendation.

10. The method of claim 4, wherein the source information includes a visual indicator of an extent of similarity to the tastes of more than individual who made a recommendation.

11. The method of claim 10, wherein the visual indicator indicates a sorting of individuals based on the extent of taste similarity.

12. The method of claim 11, wherein individuals are listed in decreasing order of similarity.

13. The method of claim 1, wherein the interestingness aspect is topical information that is new as determined by a date of emergence on the Internet.

14. The method of claim 1, wherein the interestingness aspect is topical information that is deemed by the advice facility to be new to the user.

15. The method of claim 1, wherein the interestingness aspect is topical information that has a new aspect to an existing topic.

16. The method of claim 1, wherein the interestingness aspect determined as related to the ratio of users rating the item, to the item being saved by users.

17. The method of claim 1, wherein the interestingness aspect is determined by an acceleration of social activity associated with the topical information.

18. The method of claim 1, wherein the interestingness aspect is information that is determined to be similar to a user'"'"'s profile and dissimilar to a user'"'"'s history.

19. The method of claim 18, wherein the interestingness aspect is further determined to be at least one of relevant to user'"'"'s current location and a temporal factor.

20. The method of claim 18, wherein the interestingness aspect is further based on topical information that is new as determined by a date of emergence on the Internet.

21. The method of claim 18, wherein the interestingness aspect is further based on topical information that is deemed by the advice facility to be new to the user.

22. The method of claim 18, wherein the interestingness aspect is further based on topical information that has a new aspect to an existing topic.

23. The method of claim 18, wherein the interestingness aspect is further determined as related to the ratio of users rating the item, to the item being saved by users.

24. The method of claim 18, wherein the interestingness aspect is further determined by an acceleration of social activity associated with the topical information.

25. The method of claim 1, wherein the geographical aspect is a geographical location associated with the topical information.

26. The method of claim 25, wherein the geographical location is the location of an event.

27. The method of claim 25, wherein the geographical location is the location of a store.

28. The method of claim 25, wherein the geographical location is the location of restaurant.

29. The method of claim 25, wherein the geographical location is the location of point-of-interest.

30. The method of claim 25, wherein the geographical location is at least one product location.

31. The method of claim 1, wherein the derived user taste is based on a rating.

32. The method of claim 31, wherein the rating is provided by the user.

33. The method of claim 31, wherein the rating is provided by friends of the user.

34. The method of claim 31, wherein the rating is provided by people with similar taste as the user.

35. The method of claim 31, wherein the rating is provided by an authoritative source.

36. The method of claim 31, wherein the rating is provided by a famous person.

37. The method of claim 31, wherein the rating is inferred from user behavior.

38. The method of claim 37, wherein the user behavior is online behavior, including at least one of buying behaviors, browsing behavior, social networking behavior, and location-based behaviors.

39. The method of claim 31, wherein the derived user taste is based on machine learning with respect to user online behavior.

40. The method of claim 1, wherein the recommendation is at least one of items to buy, places to visit, events to attend, and places to eat.

41. The method of claim 1, wherein the recommendation is based on one of ratings and recommendations of at least one other user with similar tastes to the user from the current geographic location of the user.

42. The method of claim 41, wherein the similar tastes is determined by machine learning through at least one of ratings from the other user and online behavior of the other user.

43. The method of claim 1, wherein the recommendation is provided as part of a feed of local discovery recommendations.

44. The method of claim 1, wherein the saving of a recommendation if saving to a list, where the list is at least one of a wish list, to-do list, an events list, a deals list.

45. The method of claim 1, wherein a saved recommendation is shown to the user through the local discovery application when the geographic location aspect of the saved recommendation matches the current location of the user.

46. The method of claim 1, wherein a recommendation is forwarded to a user based on a recommendation from at least one other user, where the advice facility determines an applicability radius around the user'"'"'s current location for use of the other user'"'"'s recommendation.

47. The method of claim 1, wherein the application is a local discovery application.

48. The method of claim 47, wherein the local discovery application correlates at least one of new and saved recommendations with the weather.

49. The method of claim 47, wherein the local discovery application correlates at least one of new and saved recommendations with the time of day.

50. The method of claim 47, wherein the local discovery application correlates at least one of new and saved recommendations with the user'"'"'s social activity.

51. The method of claim 47, wherein the local discovery application displays images based on how much the advice facility thinks the user will be interested in at least one of each recommendation and each topic.

52. The method of claim 51, wherein the displayed images are displayed in an irregular grid where the left-right top-to-bottom ordering is based on how much the advice facility thinks the user will be interested in at least one of each recommendation an each topic.