USER FEEDBACK-BASED SELECTION AND PRIORITIZING OF ONLINE ADVERTISEMENTS

• US 20110106630A1
• Filed: 11/03/2009
• Published: 05/05/2011
• Est. Priority Date: 11/03/2009
• Status: Abandoned Application

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Morgenstern, Jared, Hegeman, John
• Application Number:
  US12/611,874
• Publication Number:
  US 20110106630A1
• Time in Patent Office: Days
• US Class Current: 705/14.71
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0243 Comparative campaigns
• CPC Class Codes: G06Q 30/0245 Surveys
• CPC Class Codes: G06Q 30/0247 Calculate past, present or future revenues
• CPC Class Codes: G06Q 30/0275 Auctions
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

Advertisements to be presented to a user are selected based on feedback responses received from other users where the feedback responses represent the level of interest to the advertisements expressed by the other users. In selecting which advertisements to be presented to a user, the online service takes into account feedback responses previously collected from a group of users and revenue expected for presenting certain advertisements to the user. An online service computing device computes a total value of an advertisement based on an estimated revenue value for presenting an advertisement and a modifier representing the user'"'"'s estimated interest in the advertisements. The online service then selects or prioritizes the advertisements based on the total values. Advertisements with more positive feedback responses and/or less negative feedback responses tend to have higher total values, and therefore, such advertisements are more likely to be selected for presentation to the users.

Claims

1. A computer-implemented method for selecting advertisements to present to a user of an online service, the method comprising:

receiving a plurality of advertisements, each advertisement associated with a bid price;

receiving feedback from a group of users representing a level of interest explicitly indicated by the group of users for each of at least a subset of the advertisements;

for each of at least the subset of the advertisements;

computing an expected revenue value for presenting each advertisement to a particular user based on the bid price; and

computing a total value of each advertisement based on the expected revenue value and the feedback received for each advertisement; and

selecting one or more advertisements for presentation to the particular user from the plurality of advertisements based at least in part on the total values of the advertisements; and

sending the selected one or more advertisements for display to the particular user.

2. The method of claim 1, further comprising:

computing a modifier for each advertisement based on the feedback received from the group of users, each total value of the advertisement computed by adding the modifier for each advertisement to the expected revenue value of each advertisement.

3. The method of claim 2, wherein the modifier is computed by estimating a feedback response from the particular user based on a statistical model.

4. The method of claim 3, wherein the statistical model takes into account the feedback received from the group of users.

5. The method of claim 2, wherein computing the modifiers comprises:

obtaining a first value by multiplying a first coefficient to a number of positive feedback responses received from the group of users;

obtaining a second value by multiplying a second coefficient to a number of negative feedback responses received from the group of users; and

computing the modifiers by deducting the second value from the first value.

6. The method of claim 2, wherein computing the total values comprises adding the modifier of each advertisement to the expected revenue value of each advertisement.

7. The method of claim 1, wherein the expected revenue value is a Cost Per Impression (CPI).

8. The method of claim 1, further comprising normalizing expected revenue values by converting the expected revenue values expressed in a first cost structure into normalized expected revenue values in a second cost structure.

9. The method of claim 1, further comprising:

generating a content item responsive to receiving a request for the content item from the particular user, wherein the content item includes the selected one or more advertisements and one or more graphical user interface elements for receiving the feedback from the particular user, the generated content item sent to the particular user.

10. The method of claim 9, wherein the one or more graphical user interface elements comprises an icon for indicating a high level of interest to an advertisement by the particular user.

11. The method of claim 10, wherein the icon is a thumbs up.

12. The method of claim 11, wherein the group of users are associated with the particular user in a social networking service.

13. The method of claim 1, further comprising determining an advertisement fee for presenting an advertisement to the particular user based on the feedback for the advertisement.

14. An online service computing device for selecting advertisements for presentation to a user, comprising:

an advertisement selector configured to;

compute expected revenue calculator configured to receive a plurality of advertisements and bid prices, each bid price associated with an advertisement;

for each of at least the subset of the advertisements;

compute, based on the bid price, an expected revenue value for presenting each advertisement to a particular user;

compute a total value of each advertisement based on an expected revenue value and feedback received from a group of users for each advertisement representing a level of interest explicitly indicated by the group of users for at least a subset of the advertisements; and

select one or more advertisements for presentation to the particular user from the plurality of advertisements based at least in part on the total values of the advertisements;

a user communication module configured to;

receive the feedback from the group of users; and

sending the selected one or more advertisements for display to the particular user.

15. The online service computing device of claim 14, wherein the advertisement selector comprises a modifier calculator configured to compute a modifier for each advertisement based on the feedback received from the group of users, each total value of the advertisement computed by adding the modifier for each advertisement to the expected revenue value of each advertisement.

16. The online service computing device of claim 15, wherein the modifier is computed by estimating a feedback response from the particular user based on a statistical model.

17. The online service computing device of claim 16, wherein the statistical model takes into account the feedback received from the group of users.

18. The online service computing device of claim 15, wherein the modifier calculator is configured to:

obtain a first value by multiplying a first coefficient to a number of positive feedback responses received from the group of users;

obtain a second value by multiplying a second coefficient to a number of negative feedback responses received from the group of users; and

compute the modifiers by deducting the second value from the first value.

19. The online service computing device of claim 15, wherein the advertisement selector is configured to obtain the total value of each advertisement by adding the modifier of each advertisement to the expected revenue value of each advertisement.

20. The online service computing device of claim 14, wherein the estimated revenue is a Cost Per Impression (CPI).

21. The online service computing device of claim 14, wherein the advertisement selector is further configured to normalize the expected revenue values by converting the expected revenue values expressed in a first cost structure into normalized expected revenue values in a second cost structure.

22. The online service computing device of claim 14, further comprising a content item processor configured to:

generate a content item responsive to receiving a request for the content item from the user, wherein the content item includes the selected one or more advertisements and one or more graphical user interface elements for receiving the user feedback response from the user; and

send the generated content item to the user.

23. The online service computing device of claim 14, further comprising:

a content item processor configured to generate a content item responsive to receiving a request for the content item from the particular user, wherein the content item includes the selected one or more advertisements and one or more graphical user interface elements for receiving the feedback from the particular user, the generated content item sent to the particular user.

24. The online service computing device of claim 23, wherein the one or more graphical user interface element comprises a thumb-up icon.

25. The online service computing device of claim 14, wherein an advertisement fee charged for presenting an advertisement is determined based on the feedback for the advertisement.

26. A computer-readable storage medium storing instructions, the instructions when executed by a processor in an online service computing device for selecting advertisements for presentation to a user, causes the processor to:

receive a plurality of advertisements and bid prices each associated with an advertisement;

receive feedback from a group of users representing a level of interest explicitly indicated by the group of users for at least a subset of the advertisements;

for each of at least the subset of the advertisements;

compute an expected revenue value for presenting each advertisement to a particular user based on the bid price; and

compute total values of the advertisements based on the expected revenue value and the feedback received for each advertisement; and

select one or more advertisements for presentation to the particular user from the plurality of advertisements based at least in part on the total values of the advertisements; and

send the selected one or more advertisements for display to the particular user.