Real time personalization and categorization of entities

• US 20150170035A1
• Filed: 12/04/2013
• Published: 06/18/2015
• Est. Priority Date: 12/04/2013
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Rifkin, Ryan Michael, Singh, Sarvjeet, Anderson, John Roberts
• Application Number:
  US14/096,815
• Publication Number:
  US 20150170035A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

A user model may be generated using affinity and exposure values for each item a user interacts with in an embedded space. The user model may include exemplars which may refer to representative items in the embedded space. Based on the user model, a recommendation of items may be provided to the user. A truncated form of the user model and/or the recommended items may be sent to the user'"'"'s mobile device.

Claims

1. A method, comprising:

obtaining a plurality of exemplars, wherein each exemplar comprises a point in a vector space;

obtaining a plurality of items, wherein each item comprises a profile in the vector space;

determining a first user interaction for each of the plurality of items for a first user;

determining a first exposure for the first user for each of the plurality of exemplars;

determining a first affinity for each of the plurality of exemplars by the first user based on the first user interaction and the first exposure; and

generating a first recommendation based on the first affinity for each of the plurality of exemplars.

2. The method of claim 1, wherein the exposure indicates a first user'"'"'s preference for an exemplar.

3. The method of claim 1, further comprising:

generating a user model based on the affinity for each of the plurality of exemplars;

ranking each of the plurality of items based on the user model;

setting a diversity level; and

selecting at least one of the ranked items based on the diversity level.

4. The method of claim 1, further comprising:

generating a user model; and

sending, by a server, the user model to a mobile device of the first user.

5. The method of claim 1, further comprising:

determining a second user interaction for each of the plurality of items for a plurality of second users;

determining a second exposure for the second plurality of users for each of the plurality of exemplars;

determining a second affinity for the plurality of exemplars by the plurality second of users based on the second user interaction and the second exposure; and

generating a second recommendation based on the second affinity for each of the plurality of exemplars.

6. The method of claim 1, further comprising:

determining a weight for the first user interaction; and

modifying the first affinity based on the weight for the first user interaction

7. The method of claim 1, further comprising:

truncating the first recommendation to form a truncated recommendation list; and

providing the truncated recommendation list to a mobile device of the first user.

8. The method of claim 7, further comprising:

determining a second recommendation list for the first user;

truncating the second recommendation list to form a truncated second recommendation list; and

updating the truncated recommendation list to match the truncated second recommendation list.

9. The method of claim 8, wherein the updating is performed in real time.

10. A system, comprising:

a database for storing at least one of a first user interaction, a first exposure, a first affinity, and a first recommendation;

a processor connected to the database, the processor configured to;

obtain a plurality of exemplars, wherein each exemplar comprises a point in a vector space;

obtain a plurality of items, wherein each item comprises a profile in the vector space;

determine the first user interaction for each of the plurality of items for a first user;

determine the first exposure for the first user for each of the plurality of exemplars;

determine the first affinity for each of the plurality of exemplars by the first user based on the first user interaction and the first exposure; and

generate the first recommendation based on the first affinity for each of the plurality of exemplars.

11. The system of claim 10, wherein the exposure indicates a first user'"'"'s preference for an exemplar.

12. The system of claim 10, the processor further configured to:

generate a user model based on the affinity for each of the plurality of exemplars;

rank for each of the plurality of items based on the user model;

set a diversity level; and

select at least one of the ranked items based on the diversity level.

13. The system of claim 10, the processor further configured to:

generate a user model; and

sending, by a server, the user model to a mobile device of the first user.

14. The system of claim 10, the processor further configured to:

determine a second user interaction for each of the plurality of items for a plurality of second users;

determine a second exposure for the second plurality of users for each of the plurality of exemplars;

determine a second affinity for the plurality of exemplars by the plurality second of users based on the second user interaction and the second exposure; and

generate a second recommendation based on the second affinity for each of the plurality of exemplars.

15. The system of claim 10, the processor further configured to:

determine a weight for the first user interaction; and

modify the first affinity based on the weight for the first user interaction.

16. The system of claim 10, the processor further configured to:

truncate the first recommendation to form a truncated recommendation list; and

provide the truncated recommendation list to a mobile device of the first user.

17. The system of claim 16, the processor further configured to:

determine a second recommendation list for the first user;

truncate the second recommendation list to form a truncated second recommendation list; and

update the truncated recommendation list to match the truncated second recommendation list.

18. The system of claim 17, wherein the update is performed in real time.