Personalized recommendations based on item usage

• US 8,914,399 B1
• Filed: 06/14/2013
• Issued: 12/16/2014
• Est. Priority Date: 03/09/2011
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Paleja, Ameesh, Okereke, Mekka C., Jones, Matthew A.
• Primary Examiner(s): Ly, Anh
• Application Number:
  US13/918,593
• Time in Patent Office: 550 Days
• Field of Search: 707/767, 707/723, 707/735, 707/748, 707/732, 707/805, 707/736, 707/754, 707/722, 707/E17.059, 707/749, 707/E17.045, 707/E17.109, 707/E17.046, 705/26.5, 705/26.7, 705/26, 705/61, 705/26.61, 705/347, 705/14.67, 705/27.2, 705/26.1, 705/26.62, 705/26.63, 705/26.81, 705/26.41, 705/26.64, 705/7.29, 705/14.53, 705/14.66, 705/14.73, 705/14, 705/67, 705/27.1, 705/29, 705/27, 709/219, 709/224, 709/203, 709/217, 709/227, 455/556.1, 455/550.1
• US Class Current: 707/767
• CPC Class Codes: G06F 16/24 Querying
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/0635 Processing of requisition or of purchase orders
• CPC Class Codes: H04L 67/1097 for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]

Abstract

This disclosure describes systems and associated processes for generating recommendations for users based on usage, among other things. These systems and processes are described in the context of an interactive computing system that enables users to download applications for mobile devices (such as phones) or for other computing devices. Users'"'"' interactions with applications once they are downloaded can be observed and tracked, with such usage data being collected and provided to the interactive computing system. The interactive computing system can include a recommendation system or service that processes the usage data from a plurality of users to detect usage patterns. Using these usage patterns, among possibly other data, the recommendation system can recommend applications to users for download.

Claims

1. A method of recommending personalized computer applications to users, the method comprising:

accessing usage data associated with a mobile computing device of a user, the usage data reflecting a degree of usage by the user of a plurality of user applications installed on the mobile computing device;

identifying related applications that are related to the user applications in a usage associations data set, the usage associations data set comprising a graph having nodes that represent the related applications and weighted edges having first weights that are based at least on a degree of similar usage of the related applications by a plurality of users;

weighting the related applications with second weights that are based at least in part on the degree of usage by the user of each of the user applications to which the related applications are related in the usage associations data set, wherein said weighting causes the related applications that are related to the user applications used more frequently by the user to have greater second weights than the related applications related to the user applications used less frequently by the user; and

selecting a subset of the related applications for presentation as recommendations to the user based at least in part on the second weights;

wherein at least said identifying is performed by one or more processors.

2. The method of claim 1, wherein the usage data reflects one or more of the following:

a selected frequency that the user applications were used on the mobile computing device; and

a selected amount of time that the user applications were used on the mobile computing device.

3. The method of claim 2, wherein said identifying the related applications comprises identifying an application that was used at a similar frequency as the selected frequency.

4. The method of claim 2, wherein said identifying the related applications comprises identifying an application that was used at a similar amount of time as the selected amount of time.

5. The method of claim 1, wherein the usage data reflects a selected location in which the one or more user applications were used.

6. The method of claim 5, wherein said identifying the related applications comprises identifying an application that was used at a related location to the selected location.

7. The method of claim 1, wherein said identifying the related applications comprises identifying an application that was used with a related device to the mobile computing device.

8. The method of claim 1, wherein said identifying the related applications comprises identifying an application that was used at a related time period as a selected one of the user applications.

9. A system for recommending personalized computer applications to users, the system comprising:

a recommendation service comprising computer hardware, the recommendation service comprising a usage recommender configured to;

access usage data associated with a mobile computing device of a user, the usage data reflecting a degree of usage by the user of a plurality of user applications installed on the mobile computing device;

identify, by a computing device, related applications that are related to the user applications in a usage associations data set, the usage associations data set comprising a graph having nodes that represent the related applications and weighted edges having first weights that are based at least on a degree of similar usage of the related applications by a plurality of users;

weight the related applications with second weights that are based at least in part on the degree of usage by the user of each of the user applications to which the related applications are related in the usage associations data set; and

select a subset of the related applications for presentation as recommendations to the user based at least in part on the second weights weightings.

10. The system of claim 9, wherein the recommendation service is further configured to assign greater second weights to the related applications that are related to the user applications used more frequently by the user than to the related applications related to the user applications used less frequently by the user.

11. The system of claim 9, wherein the usage data reflects one or more of the following:

a selected frequency that the user applications were used on the mobile computing device; and

a selected amount of time that the user applications were used on the mobile computing device.

12. The system of claim 11, wherein the usage recommender is further configured to identify the related applications by at least identifying an application that was used at a similar frequency as the selected frequency.

13. The system of claim 11, wherein the usage recommender is further configured to identify the related applications by at least identifying an application that was used at a similar amount of time as the selected amount of time.

14. The system of claim 9, wherein the usage data reflects a selected location in which the one or more user applications were used.

15. The system of claim 14, wherein the usage recommender is further configured to identify the related applications by at least identifying an application that was used at a related location to the selected location.

16. The system of claim 9, wherein the usage recommender is further configured to identify the related applications by at least identifying an application that was used with a related device to the mobile computing device.

17. The system of claim 9, wherein the usage recommender is further configured to identify the related applications by at least identifying an application that was used at a related time period as a selected one of the user applications.