AD PLACEMENT
This invention concerns optimal ad selection for Web pages by selecting and updating an attribute set, obtaining and updating an ad-attribute profile, and optimally choosing the next ad. The present invention associates a set of attributes with each customer. The attributes reflect the customers' interests and they incorporate the characteristics that impact ad selection. Similarly, the present invention associates with each ad an ad-attribute profile in order to calculate a customer's estimated ad selection probability and measure the uncertainty in that estimate. An ad selection algorithm optimally selects which ad to show based on the click probability estimates and the uncertainties regarding these estimates.
1 . (canceled)
2 . A method comprising:
determining, using at least one processor, an estimated selection probability for one or more advertisements of a plurality of advertisements, the estimated selection probability indicating a likelihood a given advertisement will be selected;
identifying an expected value for the one or more advertisements of the plurality of advertisements;
determining, using the at least one processor, an expected ad placement value for the one or more advertisements, the expected ad placement value being a function of the estimated selection probability and the expected value; and
selecting an advertisement to present based on the expected ad placement value.
3 . The method as recited in claim 2 , further comprising serving the advertisement.
4 . The method as recited in claim 3 , further comprising serving the advertisement over a communications network.
5 . The method as recited in claim 3 , further comprising serving the advertisement to a mobile device.
6 . The method as recited in claim 5 , further comprising serving the advertisement to a marketing medium on the mobile device.
7 . The method as recited in claim 6 , wherein the marketing medium comprises a webpage.
8 . The method as recited in claim 6 , wherein the marketing medium comprises a software program.
9 . The method as recited in claim 3 , further comprising serving the advertisement to a webpage.
10 . The method as recited in claim 2 , wherein the estimated selection probability for a particular advertisement is a function of an observed click-through-rate for the particular advertisement.
11 . The method as recited in claim 10 , wherein the observed click-through-rate comprises a ratio of a number of times the particular advertisement has been clicked over a number of times the particular advertisement has been shown.
12 . The method as recited in claim 2 , further comprising maintaining a user profile, the user profile including user attributes reflecting interests of a user.
13 . The method as recited in claim 12 , further comprising updating the user attributes of the user profile based on one or more of Internet sites visited by the user, advertisements selected by the user, or internet searching by the user.
14 . The method as recited in claim 12 , further comprising maintaining an advertisement profile for the one or more advertisements of the plurality of advertisements, each advertisement profile including ad-attributes that reflect how much a particular advertisement correlates to a given user attribute.
15 . The method as recited in claim 14 , wherein the estimated selection probability for the particular advertisement is a function of the consumer attributes of the user and the ad-attributes for the particular advertisement.
16 . The method as recited in claim 2 , wherein the expected value for a particular advertisement is based on a value for placement or selection of the particular advertisement.
17 . A non-transitory computer-readable storage medium including a set of instructions that, when executed, cause at least one processor to perform steps comprising:
determining an estimated selection probability for one or more advertisements of a plurality of advertisements, the estimated selection probability indicating a likelihood a given advertisement will be selected;
identifying an expected value for the one or more advertisements of the plurality of advertisements;
determining an expected ad placement value for the one or more advertisements, the expected ad placement value being a function of the estimated selection probability and the expected value; and
selecting an advertisement to present based on the expected ad placement value.
18 . The computer-readable storage medium as recited in claim 17 , further comprising instructions that, when executed, cause at least one processor to serve the advertisement.
19 . The computer-readable storage medium as recited in claim 18 , further comprising instructions that, when executed, cause at least one processor to serve the advertisement over a communications network.
20 . The computer-readable storage medium as recited in claim 18 , further comprising instructions that, when executed, cause at least one processor to serve the advertisement to a mobile device.
21 . The computer-readable storage medium as recited in claim 20 , further comprising instructions that, when executed, cause at least one processor to serve the advertisement to a marketing medium on the mobile device.
22 . The computer-readable storage medium as recited in claim 21 , wherein the marketing medium comprises a webpage.
23 . The computer-readable storage medium as recited in claim 21 , wherein the marketing medium comprises a software program.
24 . The computer-readable storage medium as recited in claim 18 , further comprising instructions that, when executed, cause at least one processor to serve the advertisement to a webpage.
25 . The computer-readable storage medium as recited in claim 17 , wherein the estimated selection probability for a particular advertisement is a function of an observed click-through-rate for the particular advertisement.
26 . The computer-readable storage medium as recited in claim 25 , wherein the observed click-through-rate comprises a ratio of a number of times the particular advertisement has been clicked over a number of times the particular advertisement has been shown.
27 . The computer-readable storage medium as recited in claim 17 , further comprising instructions that, when executed, cause at least one processor to maintain a user profile, the user profile including user attributes reflecting interests of a user.
28 . The computer-readable storage medium as recited in claim 27 , further comprising instructions that, when executed, cause at least one processor to update the user attributes of the user profile based on one or more of Internet sites visited by the user, advertisements selected by the user, or internet searching by the user.
29 . The computer-readable storage medium as recited in claim 27 , further comprising instructions that, when executed, cause at least one processor to maintain an advertisement profile for the one or more advertisements of the plurality of advertisements, each advertisement profile including ad-attributes that reflect how much a particular advertisement correlates to a given user attribute.
30 . The computer-readable storage medium as recited in claim 29 , wherein the estimated selection probability for the particular advertisement is a function of the consumer attributes of the user and the ad-attributes for the particular advertisement.
31 . The computer-readable storage medium as recited in claim 17 , wherein the expected value for a particular advertisement is based on placement of the particular advertisement.
32 . The computer-readable storage medium as recited in claim 17 , wherein the expected value for a particular advertisement is based on a value for placement or selection of the particular advertisement.
33 . A method comprising:
calculating, using at least one processor, a probability that each advertisement of a plurality of advertisements will be selected;
calculating, using the at least one processor, an expected value for each advertisement of the plurality of advertisements, the expected value for a particular advertisement being a function of the probability for the particular advertisement and the expected value for the particular advertisement;
selecting an advertisement based on the expected value; and
serving the selected advertisement to a mobile device.
34 . The method as recited in claim 33 , further comprising serving the selected advertisement to a marketing medium on the mobile device.
35 . The method as recited in claim 34 , wherein the marketing medium comprises a software program on the mobile device.
36 . The method as recited in claim 35 , wherein the marketing medium comprises a webpage displayed in a web browser.
37 . The method as recited in claim 33 , wherein the probability for a given advertisement is based on a correlation of a user profile and one or more characteristics of the given advertisement.
38 . The method as recited in claim 37 , further comprising:
receiving a response to the selected advertisement; and
updating the user profile based on the response to the selected advertisement.
39 . The method as recited in claim 38 , wherein the response is a user selection of the selected advertisement.
40 . The method as recited in claim 38 , further comprising determining an updated probability that each advertisement of the plurality of advertisements will be selected by the user, the updated probability being determined using the updated user profile.
41 . The method as recited in claim 40 , further comprising:
determining an updated expected value for each advertisement by multiplying the updated probability for each advertisement by an expected value for each advertisement; and
serving the advertisement with the highest updated expected value to the user.
42 . A non-transitory computer-readable storage medium including a set of instructions that, when executed, cause at least one processor to perform steps comprising:
calculating a probability that each advertisement of a plurality of advertisements will be selected;
calculating an expected value for each advertisement of the plurality of advertisements, the expected value for a particular advertisement being a function of the probability for the particular advertisement and the expected value for the particular advertisement;
selecting an advertisement based on the expected value; and
serving the selected advertisement.
43 . The computer-readable storage medium as recited in claim 42 , further comprising instructions that, when executed, cause at least one processor to serve the selected advertisement to a marketing medium.
44 . The computer-readable storage medium as recited in claim 43 , wherein the marketing medium comprises a software program.
45 . The computer-readable storage medium as recited in claim 44 , wherein the marketing medium comprises a webpage displayed in a web browser.
46 . The computer-readable storage medium as recited in claim 42 , wherein the probability for a given advertisement is based on a correlation of a user profile and one or more characteristics of the given advertisement.
47 . The computer-readable storage medium as recited in claim 46 , further comprising instructions that, when executed, cause at least one processor to update the user profile based on a response to the selected advertisement.
48 . The computer-readable storage medium as recited in claim 47 , wherein the response is a selection by a user of the selected advertisement.
49 . The computer-readable storage medium as recited in claim 47 , further comprising instructions that, when executed, cause at least one processor to determine an updated probability that each advertisement of the plurality of advertisements will be selected by the user, the updated probability being determined using the updated user profile.
50 . The computer-readable storage medium as recited in claim 49 , further comprising instructions that, when executed, cause at least one processor to:
determine an updated expected value for each advertisement by multiplying the updated probability for each advertisement by an expected value for each advertisement; and
serve the advertisement with the highest updated expected value to the user.