FILTERING PLACEMENT OF ADVERTISEMENTS
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 . A method comprising:
receiving an advertisement request in response to one or more actions by a user on a marketing medium;
determining, using at least one processor, an estimated selection probability for one or more advertisements of a plurality of advertisements, wherein the estimated selection probability indicates a likelihood that the user will select a given advertisement;
identifying an advertisement with a high estimated selection probability;
determining if the advertisement with the high estimated selection probability is on a block list associated with the marketing medium;
if the advertisement with the high estimated selection probability is on the block list, selecting another advertisement to serve to the marketing medium; and
if the advertisement with the high estimated selection probability is not on the block list, serving the advertisement with the high estimated selection probability to the marketing medium.
2 . The method as recited in claim 1 , wherein the block list comprises one or more advertisements from a competitor.
3 . The method as recited in claim 1 , wherein the block list comprises a category of advertisements.
4 . The method as recited in claim 3 , wherein the category comprises advertisements for explicit content.
5 . The method as recited in claim 1 , further comprising maintaining a user profile, the user profile including user attributes reflecting interests of the user.
6 . The method as recited in claim 5 , 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.
7 . The method as recited in claim 6 , 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 an advertisement correlates to a given user attribute.
8 . The method as recited in claim 7 , wherein the estimated selection probability for a particular advertisement is a function of the consumer attributes of the user and the advertisement profile for the particular advertisement.
9 . The method as recited in claim 1 , wherein the marketing medium comprises a software program.
10 . The method as recited in claim 9 , wherein the marketing medium comprises one or more websites.
11 . The method as recited in claim 9 , wherein the marketing medium is a software program on a mobile device.
12 . The method as recited in claim 1 , wherein the marketing medium comprises a mobile device.
13 . The method as recited in claim 1 , wherein the one or more actions comprise accessing the marketing medium.
14 . A non-transitory computer-readable storage medium including a set of instructions that, when executed, cause at least one processor to perform steps comprising:
receiving an advertisement request in response to one or more actions by a user on a marketing medium;
determining an estimated selection probability for one or more advertisements of a plurality of advertisements, wherein the estimated selection probability indicates a likelihood that the user will select a given advertisement;
identifying an advertisement with a high estimated selection probability;
determining if the advertisement with the high estimated selection probability is on a block list associated with the marketing medium;
if the advertisement with the high estimated selection probability is on the block list, selecting another advertisement to serve to the marketing medium; and
if the advertisement with the high estimated selection probability is not on the block list, serving the advertisement with the high estimated selection probability to the marketing medium.
15 . The computer-readable storage medium as recited in claim 14 , wherein the block list comprises one or more advertisements from a competitor.
16 . The computer-readable storage medium as recited in claim 14 , wherein the block list comprises a category of advertisements.
17 . The computer-readable storage medium as recited in claim 16 , wherein the category comprises advertisements for explicit content.
18 . The computer-readable storage medium as recited in claim 14 , further comprising instructions that, when executed, cause the at least one processor to maintain a user profile, the user profile including user attributes reflecting interests of the user.
19 . The computer-readable storage medium as recited in claim 18 , further comprising instructions that, when executed, cause the 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.
20 . The computer-readable storage medium as recited in claim 19 , further comprising instructions that, when executed, cause the 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 an advertisement correlates to a given user attribute.
21 . The computer-readable storage medium as recited in claim 20 , wherein the estimated selection probability for a particular advertisement is a function of the consumer attributes of the user and the advertisement profile for the particular advertisement.
22 . The computer-readable storage medium as recited in claim 14 , wherein the marketing medium comprises a software program.
23 . The computer-readable storage medium as recited in claim 22 , wherein the marketing medium comprises one or more websites.
24 . The computer-readable storage medium as recited in claim 22 , wherein the marketing medium is a software program on a mobile device.
25 . The computer-readable storage medium as recited in claim 14 , wherein the marketing medium comprises a mobile device.
26 . The computer-readable storage medium as recited in claim 14 , wherein the one or more actions comprise accessing the marketing medium.
27 . A method comprising:
receiving an advertisement request in response to one or more actions of a user on a marketing medium;
determining, using at least one processor, one or more interests of the user;
selecting a first advertisement targeted to the one or more interests;
identifying one or more categories associated with the marketing medium; and
if the one or more categories is a permitted category, serving the first advertisement to the marketing medium.
28 . The method as recited in claim 27 , wherein if the one or more categories is not a permitted category, serving another advertisement to the marketing medium.
29 . The method as recited in claim 27 , further comprising receiving, from an advertiser associated with the first advertisement, a list of one or more permitted categories.
30 . The method as recited in claim 28 , wherein the one or more categories comprise multi-levels of categories.
31 . The method as recited in claim 30 , wherein the one or more categories comprise a sub-set of advertisements in a first category.
32 . The method as recited in claim 27 , further comprising maintaining a user profile, the user profile including user attributes reflecting interests of the user.
33 . The method as recited in claim 32 , 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.
34 . The method as recited in claim 32 , further comprising maintaining an advertisement profile for one or more advertisements of the plurality of advertisements, each advertisement profile including ad-attributes that reflect how much an advertisement correlates to a given user attribute.
35 . The method as recited in claim 34 , further comprising:
determining an estimated selection probability for the one or more advertisements of the plurality of advertisements; wherein:
the estimated selection probability indicates a likelihood that the user will select a given advertisement;
the estimated section probability is a function of the consumer attributes associated with the user and the advertisement profile for the given advertisement.
36 . The method as recited in claim 35 , wherein selecting a first advertisement related to the one or more interests comprises selecting an advertisement with the highest estimated selection probability.
37 . The method as recited in claim 27 , wherein the marketing medium comprises a software program.
38 . The method as recited in claim 37 , wherein the marketing medium comprises one or more websites.
39 . The method as recited in claim 37 , wherein the marketing medium is a software program on a mobile device.
40 . The method as recited in claim 27 , wherein the marketing medium comprises a mobile device.
41 . The method as recited in claim 27 , wherein the one or more actions comprise accessing the marketing medium.
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:
receiving an advertisement request in response to one or more actions of a user on a marketing medium;
determining one or more interests of the user;
selecting a first advertisement targeted to the one or more interests;
identifying one or more categories associated with the marketing medium; and
if the one or more categories is a permitted category, serving the first advertisement to the marketing medium.
43 . The computer-readable storage medium as recited in claim 42 , wherein if the one or more categories is not a permitted category, serving another advertisement to the marketing medium.
44 . The computer-readable storage medium as recited in claim 42 , further comprising instructions that, when executed, cause the at least one processor to associate one or more categories received from an advertiser with the first advertisement.
45 . The computer-readable storage medium as recited in claim 43 , wherein the block list comprises a category of advertisements.
46 . The computer-readable storage medium as recited in claim 45 , wherein the one or more categories comprise multi-levels of categories.
47 . The computer-readable storage medium as recited in claim 46 , wherein the one or more categories comprise a sub-set of advertisements in a first category.
48 . The computer-readable storage medium as recited in claim 42 , further comprising instructions that, when executed, cause the at least one processor to maintain a user profile, the user profile including user attributes reflecting interests of the user.
49 . The computer-readable storage medium as recited in claim 48 , further comprising instructions that, when executed, cause the 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.
50 . The computer-readable storage medium as recited in claim 48 , further comprising instructions that, when executed, cause the at least one processor to maintain an advertisement profile for one or more advertisements of the plurality of advertisements, each advertisement profile including ad-attributes that reflect how much an advertisement correlates to a given user attribute.
51 . The computer-readable storage medium as recited in claim 50 , further comprising instructions that, when executed, cause the at least one processor to determine an estimated selection probability for the one or more advertisements of the plurality of advertisements; wherein:
the estimated selection probability indicates a likelihood that the user will select a given advertisement; and
the estimated section probability is a function of the consumer attributes associated with the user and the advertisement profile for the given advertisement.
52 . The computer-readable storage medium as recited in claim 51 , wherein selecting a first advertisement related to the one or more interests comprises selecting an advertisement with the highest estimated selection probability.
53 . The computer-readable storage medium as recited in claim 42 , wherein the marketing medium comprises a software program.
54 . The computer-readable storage medium as recited in claim 53 , wherein the marketing medium comprises one or more websites.
55 . The computer-readable storage medium as recited in claim 53 , wherein the marketing medium is a software program on a mobile device.
56 . The computer-readable storage medium as recited in claim 42 , wherein the marketing medium comprises a mobile device.
57 . The computer-readable storage medium as recited in claim 42 , wherein the one or more actions comprise accessing the marketing medium.
58 . A method comprising:
receiving an advertisement request in response to one or more actions of a user on marketing medium on a mobile device;
determining, using at least one processor, an estimated selection probability for one or more advertisements of a plurality of advertisements, wherein the estimated selection probability indicates a likelihood that the user will select a given advertisement;
identifying an advertisement with a high estimated selection probability;
if the advertisement is a permitted advertisement, serving the advertisement to the mobile device.
59 . The method as recited in claim 58 , further comprising, determining, using the at least one processor, if the advertisement is a permitted advertisement.
60 . The method as recited in claim 59 , wherein determining if the advertisement is a permitted advertisement comprises determining if the advertisement is on a block list associated with the marketing medium.
61 . The method as recited in claim 59 , wherein determining if the advertisement is a permitted advertisement comprises determining if the marketing medium is on a block list associated with the advertisement.
62 . The method as recited in claim 58 , wherein the marketing medium comprises a software program.
63 . The method as recited in claim 62 , wherein the marketing medium comprises a website.
64 . The method as recited in claim 62 , further comprising maintaining a user profile, the user profile including user attributes reflecting interests of the user.
65 . The method as recited in claim 64 , 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.
66 . The method as recited in claim 65 , 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 an advertisement correlates to a given user attribute.
67 . The method as recited in claim 66 , wherein the estimated selection probability for a particular advertisement is a function of the consumer attributes of the user and the advertisement profile for the particular advertisement.