RECOMMENDATION GENERATION SYSTEMS, APPARATUS AND METHODS

• US 20090163183A1
• Filed: 09/25/2008
• Published: 06/25/2009
• Est. Priority Date: 10/04/2007
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: PEGUM, Andrew, O'DONOGHUE, Hugh, CORRIGAN, Sean, CROWE, Shane, LILLYWHITE, Kurt David
• Application Number:
  US12/237,864
• Publication Number:
  US 20090163183A1
• Time in Patent Office: Days
• US Class Current: 455/414.100
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04M 2203/655 Combination of telephone service and social networking messaging within social networks H04L51/52
• CPC Class Codes: H04M 7/0024 Services and arrangements where telephone services are combined with data services arrangements which combine a telephonic equipment and a computer H04M7/0012

Abstract

A method for generating recommendations for a user of a mobile device is provided. The user is associated with a service provider. A request for a recommendation is obtained. Data associated with the user and data on the content available to the user is retrieved from the service provider. A list of recommendations is generated based on an analysis of the retrieved user data. The recommendations are generated by a plurality of different recommendation techniques.

Claims

1. A method for generating recommendations for a user of a mobile device, the mobile device associated with a service provider, the method comprising:

obtaining a request for a recommendation;

retrieving data associated with a user and data on content available for a mobile device from a service provider;

generating a plurality of recommendations based on an analysis of the retrieved data, wherein the recommendations are generated by a plurality of different recommendation techniques; and

selecting a subset of the generated plurality of recommendations based upon filtering constraints.

2. The method of claim 1, further comprising delivering the generated recommendations to a user interface accessible by the user.

3. The method of claim 2, wherein the delivering the selected subset of recommendations further comprises delivering the selected subset of recommendations to a portal associated with the service provider.

4. The method of claim 3, further comprising:

detecting user interaction with a selected portion of the portal;

defining the filtering constraints as associated to an aspect of the selected portion with the plurality of recommendations; and

selectively displaying the subset of recommendations in response to user access of different portions of the portal.

5. The method of claim 2, wherein the delivering the generated recommendations to the user interface accessible by the user further comprises delivering the recommendations to a mobile device.

6. The method of claim 5, wherein the delivering to the mobile device is via a wireless application protocol (WAP) push message to a portal associated with the service provider.

7. The method of claim 5, wherein the delivering to the mobile device is via a short message service (SMS) message.

8. The method of claim 5, wherein the delivering to the mobile device is via a multimedia messaging service (MMS) message displayed on the mobile device.

9. The method of claim 1, wherein the generating the plurality of recommendations further comprises:

providing retrieved data to each of the different recommendation techniques to generate recommendations, each recommendation generated having an associated confidence level; and

combining the recommendations from each of the recommendation techniques in order of confidence level.

10. The method of claim 9 further comprising:

reordering the recommendations based on user defined weightings; and

filtering the reordered recommendations.

11. The method of claim 10, further comprising:

receiving the request for recommendation containing a specific constraint; and

filtering the reordered recommendations by filtering in accordance with the specific constraint.

12. The method of claim 10, wherein the filtering the reordered recommendations further comprises removing a recommendation previously availed of by the user or already presented to the user a certain number of times.

13. The method of claim 10, wherein the filtering the reordered recommendations further comprises removing a recommendation not compatible with a mobile device.

14. The method of claim 9, further comprising normalizing the confidence levels obtained from the different recommendation techniques.

15. The method of claim 1, wherein the plurality of recommendation techniques is selected from the group consisting of an associate recommender, a compare recommender, a group recommender, a track recommender, or a network recommender.

16. The method of claim 15, wherein the network recommender further comprises:

selecting a plurality of persons from a list of users within a local network of a target user, the plurality of persons being within a specified number of degrees of separation;

determining popular content previously availed of by the selected plurality of persons; and

generating recommendations based on the determined service provider and popular content.

17. The method of claim 16, wherein selecting the plurality of persons further comprises identifying the users from the local network of the target user who are associated with high weighting values.

18. The method of claim 17, wherein the weighting values are assigned by:

retrieving person-to-person mobile communication data for the user from the service provider;

filtering the person-to-person communication data to remove unwanted communication data; and

assigning a weighting value to each of the filtered person to person aggregate communications, wherein the assigned value is proportional to quantity and type of person-to-person communication activity.

19. The method of claim 18, wherein the filtering the person-to-person communication data further comprises:

removing communication data from unwanted sources.

20. The method of claim 19, wherein the unwanted sources are identified by the respective unique phone numbers.

21. The method of claim 18, wherein the filtering the person-to-person communication further comprises removing communication data that are unwanted due to type or duration of communication.

22. The method of claim 18, wherein the filtering further comprises removing communications data that are unwanted due to time or day of communication.

23. The method of claim 18, wherein the person-to-person mobile communication data further comprises one or more of voice calls, short message service (SMS) messages, multimedia messaging service (MMS) messages, or mobile communication method.

24. The method of claim 15, wherein the associate recommender further comprises:

building association rules from a user'"'"'s behavior data retrieved from a service provider; and

generating recommendations based on the built association rules.

25. The method of claim 15, wherein the compare recommender further comprises:

building links between similar content data available to the user utilizing content meta-data; and

generating recommendations based on the built links.

26. The method of claim 15, wherein the track recommender comprises:

determining a history of users activities so as to build a ranking of all content data, content data being ranked by popularity; and

generating recommendations based on the ranking.

27. The method of claim 26, wherein the activity of users comprises content purchases, ratings, or other user expressions of interest over a configurable time period.

28. The method of claim 1, wherein the data associated with the user comprises a selection of one or more of call data, date of birth, gender, prior purchases, expressions of interest, expressions of disinterest, spending pattern, mobile device type, current geographical location, call frequency, or other user metadata

29. The method of claim 28, further comprising maintaining the associated user data up to date when generating a recommendation.

30. The method of claim 1, wherein the request for a recommendation is obtained from a portal associated with a service provider.

31. The method of claim 1, wherein the recommendations are generated in real time so as not to detract from the user experience.

32. The method of claim 1, wherein recommendations are generated in a period of time less than 200 milliseconds.

33. A method for generating promotions for a user of a mobile device, the user associated with a service provider, the method comprising:

retrieving a list of promotions from a service provider;

retrieving data associated with a user and data on the content available to the user from the service provider;

generating a list of recommendations for the user by analysis of the retrieved data, wherein the recommendations are generated by a plurality of individual recommendation techniques;

selecting for delivery a subset of the retrieved promotions, the subset of retrieved promotions including promotions that are in common with the recommendations in the recommendation list and have not been already availed of by the user.

34. A computer program product, comprising:

a computer-readable storage medium comprising,

at least one instruction for causing a computer to obtain a request for a recommendation;

at least one instruction for causing the computer to retrieve data associated with a user and data on the content available to the user from the service provider; and

at least one instruction for causing the computer to generate a list of recommendations based on an analysis of the retrieved data, wherein the recommendations are generated by a plurality of different recommendation techniques.

35. A system for generating recommendations for a user of a mobile device, the user associated with a service provider, the system comprising:

means for obtaining a request for a recommendation;

means for retrieving data associated with a user and data on the content available to the user from the service provider; and

means for generating a list of recommendations based on an analysis of the retrieved data, wherein the recommendations are generated by a plurality of different recommendation techniques.

36. A system for generating recommendations for a user of a mobile device, the user associated with a service provider, the system comprising:

a profile module for storing and processing data associated with the user;

a catalogue module for storing and processing content available to the user; and

a decision module in communication with the profile module and the catalogue module, the decision module used for generating a list of recommendations for the user by analysis of data retrieved from the profile and catalogue modules, wherein the recommendations are generated by a plurality of individual recommender modules.

37. The system of claim 36, wherein the decision module includes an associate recommender, a compare recommender, a group recommender, a track recommender, and a network recommender.

38. The system of claim 37, wherein the network recommender comprises:

a call data record module, a network builder module, a network cleaning module, a weighting module, a relationship identifier module, and a network recommender module.

39. The system of claim 36, wherein the profile module comprises:

a profile database module, a profile management module, a profile grouping module, and a profile ingestion module.

40. The system of claim 36, wherein the catalogue module comprises:

a content grouping module, a searching module, a content management module, a content database module, and a content ingestion module.

41. A system for generating recommendations for a user of a mobile device, the user associated with a service provider, the system comprising:

a profile module for storing and processing data associated with the user;

a catalogue module for storing and processing content available to the user;

a decision module in communication with the profile module and the catalogue module, the decision module used for generating a list of recommendations for the user by analysis of data retrieved from the profile and catalogue modules, wherein the recommendations are generated by a plurality of individual recommender modules; and

a promote module for comparing the recommendations with a database of promotions of the service provider and for generating a list of promotions based on the comparison.

42. The system of claim 41 wherein the promote module further comprises:

a promotion management module, a promotion feedback module, a promotion creation module, a promotion retrieval module, and a promotion delivery module.

43. A method for generating recommendations for a user of a mobile device, comprising:

accessing attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;

generating recommendations for content to offer based on the attribute data and generating recommendations for content to offer based on the behavior data;

selecting a subset of recommendations by applying a filtering constraint; and

transmitting the subset of recommendations to at least a subset of the plurality of mobile devices.

44. The method of claim 43, further comprising receiving a request for recommended users for a selected content item.

45. The method of claim 43, further comprising applying a filtering constraint by accessing an exclusion constraint.

46. The method of claim 45, further comprising accessing the exclusion constraint by,

tracking a number of offerings of a selected content item to a selected user;

and

excluding further offerings of the selected content item to the selected user in response to reaching a threshold.

47. The method of claim 45, further comprising accessing an exclusion constraint by receiving a category restriction for a selected user.

48. The method of claim 45, further comprising accessing an exclusion constraint by determining that a selected content item has previously been selected and received by a selected mobile device of a selected user.

49. The method of claim 48, further comprising identifying a content item for recommendation that is associated with the previously selected content item.

50. The method of claim 45, further comprising accessing an exclusion constraint by determining device compatibility of a selected wireless device for a selected content item.

51. The method of claim 43, further comprising selecting a subset of recommendations by applying a filtering constraint by determining a confidence level of a plurality of recommendations of selected content items and applying a weighting factor in accordance to the confidence level.

52. The method of claim 51, further comprising sorting the plurality of recommendations in accordance to the confidence level.

53. The method of claim 43, further comprising the immediate updating of behavior data for a selected user of a wireless device based upon user interaction with presented offerings of recommendations.

54. The method of claim 43, further comprising associating a selected user of a wireless device with a group of users and selecting recommendations based upon the group association.

55. The method of claim 54, further comprising accessing attribute data and behavior data consisting of call data, date of birth, gender, prior purchases, expressions of interest, expressions of disinterest, spending pattern, mobile device type, current geographical location, call frequency, or other user metadata

56. At least one processor for generating recommendations for a user of a mobile device, comprising:

a first module for accessing attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;

a second module for generating recommendations for content to offer based on the attribute data and generating recommendations for content to offer based on the behavior data;

a third module for selecting a subset of recommendations by applying a filtering constraint; and

a fourth module for transmitting the subset of recommendations to at least a subset of the plurality of mobile devices.

57. A computer program product for generating recommendations for a user of a mobile device, comprising:

a computer-readable storage medium comprising,

at least one instruction for causing a computer to access attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;

at least one instruction for causing the computer to generate recommendations for content to offer based on the attribute data and to generate recommendations for content to offer based on the behavior data;

at least one instruction for causing the computer to select a subset of recommendations by applying a filtering constraint; and

at least one instruction for causing the computer to transmit the subset of recommendations to at least a subset of the plurality of mobile devices.

58. An apparatus for generating recommendations for a user of a mobile device, comprising:

means for accessing attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;

means for generating recommendations for content to offer based on the attribute data and generating recommendations for content to offer based on the behavior data;

means for selecting a subset of recommendations by applying a filtering constraint; and

means for transmitting the subset of recommendations to at least a subset of the plurality of mobile devices.

59. An apparatus for generating recommendations for a user of a mobile device, comprising:

a profile storage component containing attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;

a profile and recommendation system for generating recommendations for content to offer based on accessed attribute data, for generating recommendations for content to offer based on accessed behavior data, and for selecting a subset of recommendations by applying a filtering constraint; and

a network communication module for transmitting the subset of recommendations to at least a subset of the plurality of mobile devices.

60. The apparatus of claim 59, further comprising the network communication module for receiving a request for recommended users for a selected content item.

61. The apparatus of claim 60, further comprising the profile and recommendation system for applying a filtering constraint by accessing an exclusion constraint.

62. The apparatus of claim 61, further comprising the profile and recommendation system for accessing an exclusion constraint by,

tracking a number of offerings of a selected content item to a selected user;

and

excluding further offerings of the selected content item to the selected user in response to reaching a threshold.

63. The apparatus of claim 61, further comprising the profile and recommendation system for accessing an exclusion constraint by receiving a category restriction for a selected user.

64. The apparatus of claim 61, further comprising the profile and recommendation system for accessing an exclusion constraint by determining that a selected content item has previously been selected and received by a selected mobile device of a selected user.

65. The apparatus of claim 64, further comprising the profile and recommendation system for identifying a content item for recommendation that is associated with the previously selected content item.

66. The apparatus of claim 61, further comprising the profile and recommendation system for accessing an exclusion constraint by determining device compatibility of a selected wireless device for a selected content item.

67. The apparatus of claim 59, further comprising the profile and recommendation system for selecting a subset of recommendations by applying a filtering constraint by determining a confidence level of a plurality of recommendations of selected content items and applying a weighting factor in accordance to the confidence level.

68. The apparatus of claim 67, further comprising the profile and recommendation system for sorting the plurality of recommendations in accordance to the weighting factor.

69. The apparatus of claim 59, further comprising the profile and recommendation system for immediate updating of behavior data for a selected user of a wireless device based upon user interaction with presented offerings of recommendations.

70. The apparatus of claim 59, further comprising the profile and recommendation system for associating a selected user of a wireless device with a group of users and selecting recommendations based upon the group association.

71. The apparatus of claim 59, further comprising the profile and recommendation system for accessing attribute data and behavior data consisting of call data, date of birth, gender, prior purchases, expressions of interest, expressions of disinterest, spending pattern, mobile device type, current geographical location, call frequency, or other user metadata