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:
associating user attributes with a user, the user attributes indicating interests of the user;
receiving information regarding one or more activities of the user on a marketing medium;
updating, using at least one processor, the user attributes based on the received information, wherein more recent information is given more weight than less recent information when updating the user attributes; and
selecting, using the at least one processor, one or more advertisements that relate to the updated user attributes to present to the user.
3 . The method as recited in claim 2 , further comprising updating the user attributes using a moving average.
4 . The method as recited in claim 2 , further comprising updating the user attributes using an exponentially-weighted approach.
5 . The method as recited in claim 2 , wherein selecting one or more advertisements comprises:
determining a probability that one or more advertisements of a plurality of advertisements will be selected by the user; and
selecting the advertisement with a high probability.
6 . The method as recited in claim 5 , wherein the probability that a given advertisement will be selected by the user is a function of a click-thru-rate for the given advertisement.
7 . The method as recited in claim 5 , further associating ad-attributes with one or more advertisements of the plurality of advertisements, the ad-attributes reflecting how much an advertisement correlates to user attributes.
8 . The method as recited in claim 7 , wherein the probability that a given advertisement will be selected by the user is a function of the user attributes and the ad-attributes for the given advertisement.
9 . The method as recited in claim 2 , wherein the marketing medium comprises one or more websites.
10 . The method as recited in claim 9 , further comprising determining in which interest categories the user commonly browses.
11 . The method as recited in claim 10 , wherein selecting one or more advertisements comprises selecting advertisements related to the interest categories in which the user commonly browses.
12 . The method as recited in claim 10 , further comprising measuring a percentage of time the user spends browsing in one or more interest categories.
13 . The method as recited in claim 2 , further comprising serving the one or more advertisements to a mobile device.
14 . The method as recited in claim 13 , wherein the marketing medium comprises a software program on the mobile device.
15 . The method as recited in claim 14 , wherein the marketing medium comprises a webpage displayed in a browser.
16 . A non-transitory computer-readable storage medium including a set of instructions that, when executed, cause at least one processor to perform steps comprising:
associating user attributes with a user, the user attributes indicating interests of the user;
receiving information regarding one or more activities of the user on a marketing medium;
updating the user attributes based on the received information, wherein more recent information is given more weight than less recent information when updating the user attributes; and
selecting one or more advertisements that relate to the updated user attributes to present to the user.
17 . The computer-readable storage medium as recited in claim 16 , further comprising instructions that, when executed, cause at least one processor to update the user attributes using a moving average.
18 . The computer-readable storage medium as recited in claim 16 , further comprising instructions that, when executed, cause at least one processor to update the user attributes using an exponentially-weighted approach.
19 . The computer-readable storage medium as recited in claim 16 , wherein selecting one or more advertisements comprises:
determining a probability that one or more advertisements of a plurality of advertisements will be selected by the user; and
selecting the advertisement with the highest probability.
20 . The computer-readable storage medium as recited in claim 19 , wherein the probability that a given advertisement will be selected by the user is a function of a click-thru-rate for the given advertisement.
21 . The computer-readable storage medium as recited in claim 19 , further comprising instructions that, when executed, cause at least one processor to associate ad-attributes with one or more advertisements of the plurality of advertisements, the ad-attributes reflecting how much an advertisement correlates to user attributes.
22 . The computer-readable storage medium as recited in claim 21 , wherein the probability that a given advertisement will be selected by the user is a function of the user attributes and the ad-attributes for the given advertisement.
23 . The computer-readable storage medium as recited in claim 16 , wherein the marketing medium comprises one or more websites.
24 . The computer-readable storage medium as recited in claim 23 , further comprising instructions that, when executed, cause at least one processor to determine in which interest categories the user commonly browses.
25 . The computer-readable storage medium as recited in claim 24 , wherein selecting one or more advertisements comprises selecting advertisements related to the interest categories in which the user commonly browses.
26 . The computer-readable storage medium as recited in claim 24 , further comprising instructions that, when executed, cause at least one processor to measure a percentage of time the user spends browsing in one or more interest categories.
27 . The computer-readable storage medium as recited in claim 16 , further comprising instructions that, when executed, cause at least one processor to serve the one or more advertisements to a mobile device.
28 . The computer-readable storage medium as recited in claim 27 , wherein the marketing medium comprises a software program on the mobile device.
29 . The computer-readable storage medium as recited in claim 28 , wherein the marketing medium comprises a webpage displayed in a browser.
30 . A method comprising:
associating, using at least one processor, attributes with a user profile, wherein the attributes indicate interests of a user associated with the user profile;
receiving first information regarding one or more activities of the user on a marketing medium during a first time period;
receiving second information regarding one or more activities of the user on a marketing medium during a second time period, the second time period being more recent than the first time period; and
updating, using the at least one processor, the attributes associated with the user profile based on the first information and the second information, wherein more weight is given to the second information than the first information.
31 . The method as recited in claim 30 , further comprising updating the attributes using a moving average.
32 . The method as recited in claim 30 , further comprising updating the attributes using an exponentially-weighted approach.
33 . The method as recited in claim 30 , further comprising selecting one or more advertisements to present to the user over a communications network, the selection of the one or more advertisements being based on the attributes associated with the user profile.
34 . The method as recited in claim 33 , wherein the communications network comprise the Internet.
35 . The method as recited in claim 33 , wherein selecting one or more advertisements comprises:
determining a probability that one or more advertisements of a plurality of advertisements will be selected by the user; and
selecting the advertisement with a high probability.
36 . The method as recited in claim 33 , further comprising serving the one or more advertisements to a mobile device.
37 . The method as recited in claim 36 , further comprising serving the one or more advertisements to the advertising media on the mobile device.
38 . The method as recited in claim 37 , wherein the advertising media comprises a software program.
39 . The method as recited in claim 30 , wherein the advertising media comprises a webpage.
40 . A non-transitory computer-readable storage medium including a set of instructions that, when executed, cause at least one processor to perform steps comprising:
associating attributes with a user profile, wherein the attributes indicate interests of a user associated with the user profile;
receiving first information regarding one or more activities of the user on a marketing medium during a first time period;
receiving second information regarding one or more activities of the user on a marketing medium during a second time period, the second time period being more recent than the first time period; and
updating the attributes associated with the user profile based on the first information and the second information, wherein more weight is given to the second information than the first information.
41 . The computer-readable storage medium as recited in claim 40 , further comprising instructions that, when executed, cause at least one processor to update the attributes using a moving average.
42 . The computer-readable storage medium as recited in claim 40 , further comprising instructions that, when executed, cause at least one processor to update the attributes using an exponentially-weighted approach.
43 . The computer-readable storage medium as recited in claim 40 , further comprising instructions that, when executed, cause at least one processor to select one or more advertisements to present to the user over a communications network, the selection of the one or more advertisements being based on the attributes associated with the user profile.
44 . The computer-readable storage medium as recited in claim 43 , wherein selecting one or more advertisements comprises:
determining a probability that one or more advertisements of a plurality of advertisements will be selected by the user; and
selecting the advertisement with the highest probability.
45 . The computer-readable storage medium as recited in claim 43 , further comprising instructions that, when executed, cause at least one processor to serve the one or more advertisements to a mobile device.
46 . The computer-readable storage medium as recited in claim 45 , further comprising instructions that, when executed, cause at least one processor to serve the one or more advertisements to the advertising media on the mobile device.
47 . The computer-readable storage medium as recited in claim 46 , wherein the advertising media comprises a software program.
48 . The computer-readable storage medium as recited in claim 47 , wherein the advertising media comprises a webpage.