Abstract: One or more keywords and/or information about one or more properties may be accepted, and a set of one or more taxonomy categories may be determined using at least some of the keyword(s) and/or property information and perhaps term co-occurrence clusters. The determined taxonomy categories may be presented to an advertising user as an ad targeting suggestion. Each taxonomy category may have at least one associated property (e.g., Web document), that participates in an advertising network. An advertiser selection of a suggested taxonomy category may be accepted, and the serving of an ad of the advertiser may be targeted to each property associated with the selected suggested taxonomy category. Alternatively, such properties may be presented to an advertising user as an ad targeting suggestion.

Abstract: One or more keywords and/or information about one or more properties may be accepted, and a set of one or more taxonomy categories may be determined using at least some of the keyword(s) and/or property information and perhaps term co-occurrence clusters. The determined taxonomy categories may be presented to an advertising user as an ad targeting suggestion. Each taxonomy category may have at least one associated property (e.g., Web document), that participates in an advertising network. An advertiser selection of a suggested taxonomy category may be accepted, and the serving of an ad of the advertiser may be targeted to each property associated with the selected suggested taxonomy category. Alternatively, such properties may be presented to an advertising user as an ad targeting suggestion.

Abstract: One or more keywords and/or information about one or more properties may be accepted, and a set of one or more taxonomy categories may be determined using at least some of the keyword(s) and/or property information and perhaps term co-occurrence clusters. The determined taxonomy categories may be presented to an advertising user as an ad targeting suggestion. Each taxonomy category may have at least one associated property (e.g., Web document), that participates in an advertising network. An advertiser selection of a suggested taxonomy category may be accepted, and the serving of an ad of the advertiser may be targeted to each property associated with the selected suggested taxonomy category. Alternatively, such properties may be presented to an advertising user as an ad targeting suggestion.

Abstract: A Website may be automatically categorized by accepting Website information, determining a set of scored clusters for the Website using the Website information, and determining at least one category of a predefined taxonomy using at least some of the set of clusters.

Abstract: One or more keywords and/or information about one or more properties may be accepted, and a set of one or more taxonomy categories may be determined using at least some of the keyword(s) and/or property information and perhaps term co-occurrence clusters. The determined taxonomy categories may be presented to an advertising user as an ad targeting suggestion. Each taxonomy category may have at least one associated property (e.g., Web document), that participates in an advertising network. An advertiser selection of a suggested taxonomy category may be accepted, and the serving of an ad of the advertiser may be targeted to each property associated with the selected suggested taxonomy category. Alternatively, such properties may be presented to an advertising user as an ad targeting suggestion.

Abstract: A computer-implemented method, system, and computer-readable medium includes determining an attribute of a web page to be displayed to a user at a location, selecting content corresponding to the determined web page attribute, and transmitting the selected content to the location for display as at least part of the web page.

Abstract: A computer-implemented method, system, and computer-readable medium includes determining an attribute of a web page to be displayed to a user at a location, selecting content corresponding to the determined web page attribute, and transmitting the selected content to the location for display as at least part of the web page.

Abstract: A computer-implemented method, system, and computer-readable medium includes determining an attribute of a web page to be displayed to a user at a location, selecting content corresponding to the determined web page attribute, and transmitting the selected content to the location for display as at least part of the web page.

Abstract: A computer-implemented method includes determining an attribute of a web page to be displayed to a user at a location, selecting content corresponding to the determined web page attribute, and transmitting the selected content to the location for display as at least part of the web page.

Abstract: Methods, computer-readable media, and systems for placement attribute targeting are described. In one example, an advertiser can specify attributes of one or more web pages on which their advertisements can be displayed. Based on a page attribute of the web page that a user accesses, one or more advertisements can be transmitted to the user for display, where the advertisements are chosen such that the page attribute matches the attribute specified by the advertiser. In some implementations, a computer-implemented method includes determining an attribute of a web page to be displayed to a user at a location, selecting content corresponding to the determined web page attribute, and transmitting the selected content to the location for display as at least part of the web page.

Abstract: A server system receives, respectively, a first character string from a first user and a second character string from a second user. There are one or more differences between the first and second character strings. The server system obtains from a plurality of previously submitted complete queries, respectively, a first set of predicted complete queries corresponding to the first character string and a second set of predicted complete queries corresponding to the second character string. There are one or more identical queries in both the first and second sets. The server system conveys at least a first subset of the first set to the first user and at least a second subset of the second set to the second user. Both the first subset and the second subset include a respective identical query.

Abstract: A Website may be automatically categorized by (a) accepting Website information, (b) determining a set of scored clusters (e.g., semantic, term co-occurrence, etc.) for the Website using the Website information, and (c) determining at least one category (e.g., a vertical category) of a predefined taxonomy using at least some of the set of clusters.

Abstract: A Website may be automatically categorized by (a) accepting Website information, (b) determining a set of scored clusters (e.g., semantic, term co-occurrence, etc.) for the Website using the Website information, and (c) determining at least one category (e.g., a vertical category) of a predefined taxonomy using at least some of the set of clusters.

Abstract: Methods and systems for distributing interest categories within a hierarchical classification, wherein a parent interest category is identified and the hierarchical classification is filtered. A sub-interest category is extracted, based on the filtering, and a weighting associated with the sub-interest category is transferred into the parent interest category. The weighting is distributed in the hierarchical classification.

Abstract: Systems, apparatus, and methods allow for revenue sharing between data providers and web page publishers. In one aspect, a computer-implemented method includes: receiving a request for alternative content based on a request for content; receiving user identifier data from at least one data provider; selecting at least one first alternative content from a first pool of alternative content that is contextually related to the requested content; determining a first cost of the at least one first alternative content; selecting, by at least one processing circuit, at least one second alternative content from the first pool, and from the second pool of alternative content that is related to the user identifier data; determining a second cost of the at least one second alternative content; and determining a pricing value of the user identifier data based on a difference between the second and the first costs.

Abstract: A demographic attribute value of a sink online document (such as Websites or Web pages) may be determined given a set of users who have visited at least one of the source documents and the sink document, by (a) accepting a value(s) of the demographic attribute, each of which values is associated with a source online document (where each of the source online documents has a value for the demographic attribute and has been visited by at least one user of the given set), (b) determining an estimate of the demographic attribute value of each of the users of the given set using the accepted demographic attribute value of each of the source online documents visited by the user, and (c) determining the demographic attribute value of the sink online document using the determined estimate of the demographic attribute value of each of the users of the given set.

Abstract: One or more keywords and/or information about one or more properties may be accepted, and a set of one or more taxonomy categories may be determined using at least some of the keyword(s) and/or property information and perhaps term co-occurrence clusters. The determined taxonomy categories may be presented to an advertising user as an ad targeting suggestion. Each taxonomy category may have at least one associated property (e.g., Web document), that participates in an advertising network. An advertiser selection of a suggested taxonomy category may be accepted, and the serving of an ad of the advertiser may be targeted to each property associated with the selected suggested taxonomy category. Alternatively, such properties may be presented to an advertising user as an ad targeting suggestion.

Abstract: One or more keywords and/or information about one or more properties may be accepted, and a set of one or more taxonomy categories may be determined using at least some of the keyword(s) and/or property information and perhaps term co-occurrence clusters. The determined taxonomy categories may be presented to an advertising user as an ad targeting suggestion. Each taxonomy category may have at least one associated property (e.g., Web document), that participates in an advertising network. An advertiser selection of a suggested taxonomy category may be accepted, and the serving of an ad of the advertiser may be targeted to each property associated with the selected suggested taxonomy category. Alternatively, such properties may be presented to an advertising user as an ad targeting suggestion.

Abstract: A demographic attribute value of a sink online document may be determined by (a) accepting a value(s) of the demographic attribute of a source online document, (b) accepting, for each of the source online documents having an accepted demographic attribute value, a probability that a user will visit or has visited the sink online document if the user visited the source online document, and (c) determining the demographic attribute value of the sink online document using the demographic attribute value of each of the source online documents and the probabilities. A demographic attribute value of the sink online document may also be determined using the above information and using the demographic attribute value of each of the source online documents and the probabilities that a user will visit or has visited the sink online document if the user visited the other online document.

Abstract: A demographic attribute value of a sink online document (such as Websites or Web pages) may be determined given a set of users who have visited at least one of the source documents and the sink document, by (a) accepting a value(s) of the demographic attribute, each of which values is associated with a source online document (where each of the source online documents has a value for the demographic attribute and has been visited by at least one user of the given set), (b) determining an estimate of the demographic attribute value of each of the users of the given set using the accepted demographic attribute value of each of the source online documents visited by the user, and (c) determining the demographic attribute value of the sink online document using the determined estimate of the demographic attribute value of each of the users of the given set.