Per-User Predictive Profiles for Personalized Advertising

• US 20110295687A1
• Filed: 05/26/2010
• Published: 12/01/2011
• Est. Priority Date: 05/26/2010
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Bilenko, Mikhail, Richardson, Matthew
• Application Number:
  US12/787,410
• Publication Number:
  US 20110295687A1
• Time in Patent Office: Days
• US Class Current: 705/14.54
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

Described is using per-user profile data (e.g., maintained in a browser cookie) as a factor in selecting advertisements to be presented to a user for a current context such as containing query keywords. For example, an advertiser may be willing to bid more if the current context'"'"'s keywords match the user profile data that indicates a particular area of interest to the user and advertiser. Also described is updating the per-user profile data with the current context if doing so increases the expected utility of the per-user profile data, e.g., increases the predicted amount of revenue from advertisement clicking. Also described is other advertisement personalization based upon the per-user profile data, e.g., the ranking and/or appearance of the advertisements.

Claims

1. In a computing environment, a method performed on at least one processor comprising, receiving per-user profile data and current context information at a content provider, selecting content based upon the per-user profile data and the current context information or user interaction, or based upon the per-user profile data, the current context information and user interaction, and updating the per-user profile data based upon the current context information.

2. The method of claim 1 wherein the content provider comprises an advertising platform, and wherein selecting the content based upon the per-user profile data and current context information comprises selecting at least one advertisement.

3. The method of claim 2 further comprising, using bid-related information and bid-increment information with respect to an advertisement, including charging an advertiser differently when selecting the advertisement based upon the bid-increment information.

4. The method of claim 1 wherein receiving the per-user profile data and current context information comprises communicating with a search engine that is processing a query, the current context information including one or more keywords in the query, or one or more keywords related to the query, or both one or more keywords in the query and one or more keywords related to the query.

5. The method of claim 1 wherein the content provider comprises an advertising platform, wherein the content corresponds to a plurality of advertisements, and further comprising, ranking the advertisements relative to one another based upon the per-user profile data.

6. The method of claim 1 wherein the content provider comprises an advertising platform, wherein the content corresponds to a plurality of advertisements, and further comprising, suppressing at least one advertisement from appearing based upon the per-user profile data.

7. The method of claim 1 further comprising, modifying presentation of the content based upon the per-user profile data.

8. The method of claim 1 further comprising, sending the per-user profile data for maintaining only on a client device.

9. The method of claim 1 wherein updating the per-user profile data comprises determining whether adding one or more keywords to the per-user profile data increases utility or modifying one or more keywords in the per-user profile data increases utility, or both adding one or more keywords to the per-user profile data increases utility and modifying one or more keywords in the per-user profile data increases utility.

10. The method of claim 9 wherein the profile data comprise a plurality of profile components, and further comprising, using machine learning for estimating a predicted utility of each profile component.

11. The method of claim 9 wherein determining whether utility is increased comprises using one or more submodular optimization methods.

12. In a computing environment, a system comprising, an advertisement selection mechanism that receives context information and profile data representative of a user, and processes the context information and profile data to select advertisements;

and

a profile update mechanism configured to update the profile data based upon the context information.

13. The system of claim 12 wherein the advertisement selection mechanism further selects advertisements based upon bid data of advertisers.

14. The system of claim 13 wherein the bid data comprises information by which an advertiser pays a different amount depending on the profile data.

15. The system of claim 12 wherein the profile data comprises a plurality of keywords, each keyword associated with time data, categorical data or match type data, or any combination of time data, categorical data or match type data.

16. The system of claim 12 wherein the profile data is maintained in a browser cookie.

17. The system of claim 12 wherein the profile update mechanism decides whether to update the profile data based upon utility computations, in which the utility computations include parameter values learned by machine learning.

18. One or more computer-readable media having computer-executable instructions, which when executed perform steps, comprising:

receiving per-user profile data and current context information at a advertising platform; and

selecting advertisements based upon advertiser bid data, the per-user profile data and current context information or user interaction, or based upon advertiser bid data, the per-user profile data and current context information and user interaction.

19. The one or more computer-readable media of claim 18 having further computer-executable instructions comprising, determining whether the current context information improves per-user profile data utility, and if so, updating the per-user profile data based upon the current context information

20. The one or more computer-readable media of claim 18 having further computer-executable instructions comprising, ranking the advertisements relative to one another based upon the per-user profile data, or changing presentation of at least one of the advertisements based upon the per-user profile data, or both ranking the advertisements relative to one another and changing the presentation of at least one of the advertisements based upon the per-user profile data.