ONLINE ADVERTISING VALUATION APPARATUS AND METHOD
A method comprising for determining the value provided to an advertiser through the advertiser's online advertisements. The method may begin with providing an online advertisement linked to content. A macro-context may be obtained that quantifies the affinity of the content for a plurality of domains corresponding to a semantic space. A personalization vector may be obtained that quantifies the affinity of at least one aspect of a user for the plurality of domains. The user may then select the online advertisement. A match value may be calculated that quantifies the similarity between the personalization vector and the macro-context. The advertiser may then be charged a monetary amount for the user's “click.” The monetary amount may be based, at least in part, on the match value.
21 . A computer-implemented method for certifying that a user who is viewing content has viewed substantially similar content at a previous interval of time comprising:
classifying content using a macro-context vector, wherein the macro-context vector specifies classification of the content within a domain;
assigning a personalization vector to the user based on browser history;
multiplying the personalization vector and the macro-context vector to receive a match value;
receiving, from a content provider, a threshold; and
comparing the match value to the threshold, wherein if the match value is greater than the threshold, generating a certification that the user who is viewing content has viewed substantially similar content at a previous interval of time.
22 . The computer-implemented method of claim 21 , wherein the content provider is an advertiser.
23 . The computer-implemented method of claim 21 , wherein the macro-context vector comprises a universal resource locator.
24 . The computer-implemented method of claim 21 , wherein the personalization vector comprises an aggregation of a plurality of vectors from viewed web pages.
25 . The computer-implemented claim of 21 , wherein the multiplication of the personalization vector and the macro-context vector comprises a dot product.
26 . The computer implemented method of claim 21 , further comprising:
providing the match value to the content provider.
27 . The computer-implemented method of claim 21 , further comprising:
based on the generation of the certification, determining a quality of visitors to a web page.
28 . The computer-implemented method of claim 21 , further comprising:
storing the match value in a repository.
29 . The computer implemented method of claim 28 , further comprising:
retrieving the match value from the repository; and
based on the match value, customizing the content for the user.
30 . The computer-implemented method of claim 21 , further comprising:
implementing a content auction, wherein a price is determined for a keyword that does not currently have a bid based on the certification.
31 . A computer-implemented method for representing a domain as a set of slots to be filled comprising:
generating a list of assertions answered by text within the domain to specify the domain itself;
verifying whether the list of text answers relate to a specific question by extracting content of interest to the domain;
based on the verification, storing a result in a database; and
generating a report based on the stored result.
32 . The computer-implemented method of claim 31 , wherein the list of assertions specifies differences between domains.
33 . The computer-implemented method of claim 31 , wherein the list of assertions is matched to a previously generated list of assertions.
34 . The computer-implemented method of claim 31 , further comprising:
matching the generated list of assertions with the domain.
35 . The computer-implemented method of claim 31 , further comprising:
selecting from the list of assertions for the domain; and
creating relationships between the list of assertions answered by text.
36 . The computer-implemented method of claim 35 , wherein the relationships comprise at least one of adjacency, font size, paragraph length, verb type coexistence, voice, tense, keywords, key phrases or numerical values.
37 . A system comprising:
a tangible computer-readable storage device comprising instructions; and one or more processors coupled to the tangible computer-readable storage device and configured to execute the instructions to perform operations comprising:
classifying content using a macro-context vector, wherein the macro-context vector specifies classification of the content within a domain;
assigning a personalization vector to the user based on browser history, wherein the personalization vector comprises an aggregation of macro-context vectors;
multiplying the personalization vector and the macro-context vector to receive a match value;
receiving, from a content provider, a threshold; and
comparing the match value to the threshold, wherein if the match value is greater than the threshold, generating a certification that the user who is viewing content has viewed substantially similar content at a previous interval of time.
38 . The system of claim 37 , the operations further comprising:
storing the match value in a repository.
39 . The system of claim 38 , the operations further comprising:
retrieving the match value from the repository; and
based on the match value, customizing the content for the user.
40 . The system of claim 37 , the operations further comprising:
implementing a content auction, wherein a price is determined for a keyword that does not currently have a bid based on the certification.