Category similarities
First Claim
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1. A computer-implemented method, comprising:
- for each of a plurality of user identifiers;
identifying event data for a user identifier based on past user sessions associated with the user identifier, the event data specifying events that occurred during the past user sessions;
categorizing each of the events as belonging to one or more vertical categories;
for each vertical category, determining a user identifier interest weight for the user identifier based on the events associated with the vertical category;
for each pair of vertical categories, generating, by one or more processors, asymmetric association data representative of an asymmetric association between a first vertical category and a second vertical category of the pair of vertical categories based on the user identifier interest weights, the asymmetric association data being a pair of a first asymmetric similarity measure and a second asymmetric similarity measure, wherein;
the first asymmetric similarity measure is based on an asymmetric similarity measure of the first vertical category with respect to the second vertical category;
the second asymmetric similarity measure is based on an asymmetric similarity measure of the second vertical category with respect to the first vertical category; and
the first asymmetric similarity measure is different from the second asymmetric similarity measure;
for each pair of vertical categories, generating symmetric association data representative of a symmetric association between the first and second vertical categories of the pair of vertical categories, the symmetric association being a pair of equal similarity measures with a first equal similarity measure of the pair being a symmetric similarity measure of the first vertical category with respect to the second vertical category and a second equal similarity measure of the pair being a symmetric similarity measure of the second vertical category with respect to the first vertical category where each equal similarity measure is based on the corresponding first and second asymmetric similarity measures for the first and second vertical categories, and wherein the first equal similarity measure is equal to the second equal similarity measure; and
selecting advertisements for user sessions associated with a user identifier based on the symmetric association data.
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Abstract
Methods, systems, and apparatus for determining similarity measures between vertical categories based on users'"'"' online activities. The similarity measures are symmetric similarity measures based on both a similarity measure of a first vertical category relative to a second vertical category and a similarity measure of the second vertical category relative to the first vertical category.
10 Citations
22 Claims
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1. A computer-implemented method, comprising:
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for each of a plurality of user identifiers; identifying event data for a user identifier based on past user sessions associated with the user identifier, the event data specifying events that occurred during the past user sessions; categorizing each of the events as belonging to one or more vertical categories; for each vertical category, determining a user identifier interest weight for the user identifier based on the events associated with the vertical category; for each pair of vertical categories, generating, by one or more processors, asymmetric association data representative of an asymmetric association between a first vertical category and a second vertical category of the pair of vertical categories based on the user identifier interest weights, the asymmetric association data being a pair of a first asymmetric similarity measure and a second asymmetric similarity measure, wherein; the first asymmetric similarity measure is based on an asymmetric similarity measure of the first vertical category with respect to the second vertical category; the second asymmetric similarity measure is based on an asymmetric similarity measure of the second vertical category with respect to the first vertical category; and the first asymmetric similarity measure is different from the second asymmetric similarity measure; for each pair of vertical categories, generating symmetric association data representative of a symmetric association between the first and second vertical categories of the pair of vertical categories, the symmetric association being a pair of equal similarity measures with a first equal similarity measure of the pair being a symmetric similarity measure of the first vertical category with respect to the second vertical category and a second equal similarity measure of the pair being a symmetric similarity measure of the second vertical category with respect to the first vertical category where each equal similarity measure is based on the corresponding first and second asymmetric similarity measures for the first and second vertical categories, and wherein the first equal similarity measure is equal to the second equal similarity measure; and selecting advertisements for user sessions associated with a user identifier based on the symmetric association data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented method, comprising:
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determining user identifier interest weights for user identifiers in a plurality of vertical categories based on events that occurred during past user sessions for the user identifiers; for each pair of vertical categories, generating, by one or more processors, asymmetric association data representative of an asymmetric association between a first vertical category and a second vertical category of the pair of vertical categories based on the user identifier interest weights, the asymmetric association data being a pair of a first asymmetric similarity measure and a second asymmetric similarity measure, wherein; the first asymmetric similarity measure is based on an asymmetric similarity measure of the first vertical category with respect to the second vertical category; the second asymmetric similarity measure is based on an asymmetric similarity measure of the second vertical category with respect to the first vertical category; and the first asymmetric similarity measure is different from the second asymmetric similarity measure; for each pair of vertical categories, generating symmetric association data representative of a symmetric association of a first vertical category with a second vertical category, the symmetric association being a pair of equal similarity measures where an equal similarity measure is based on the corresponding first and second asymmetric similarity measures, wherein each equal similarity measure of the pair is a same equal similarity measure; identifying a web page presented on a user device during a user session; identifying a vertical category to which the web page belongs; determining a vertical category similar to the vertical category to which the web page belongs based on the symmetric association data; selecting an advertisement belonging to the determined vertical category; and providing the advertisement for display on the user device. - View Dependent Claims (12, 13, 14)
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15. A system, comprising:
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one or more data processing devices; software stored on a computer storage apparatus and comprising instructions executable by the one or more data processing devices and upon such execution cause the one or more data processing devices to define; a user interest module configured to; for each of a plurality of user identifiers; identify event data for a user identifier based on past user sessions associated with the user identifier, the event data specifying events that occurred during the past user sessions; categorize each of the events as belonging to one or more vertical categories; and for each vertical category, determine a user identifier interest weight for the user identifier based on the events associated with the vertical category; a symmetric association module configured to; for each pair of vertical categories, generating asymmetric association data representative of an asymmetric association between a first vertical category and a second vertical category of the pair of vertical categories based on the user identifier interest weights, the asymmetric association data being a pair of a first asymmetric similarity measure and a second asymmetric similarity measure, wherein; the first asymmetric similarity measure is based on an asymmetric similarity measure of the first vertical category with respect to the second vertical category; the second asymmetric similarity measure is based on an asymmetric similarity measure of the second vertical category with respect to the first vertical category; and the first asymmetric similarity measure is different from the second asymmetric similarity measure; for each pair of vertical categories, generating symmetric association data representative of a symmetric association between the first and second vertical categories of the pair of vertical categories, the symmetric association being a pair of equal similarity measures with a first equal similarity measure of the pair being a symmetric similarity measure of the first vertical category with respect to the second vertical category and a second equal similarity measure of the pair being a symmetric similarity measure of the second vertical category with respect to the first vertical category where each equal similarity measure is based on the corresponding first and second asymmetric similarity measures for the first and second vertical categories, and wherein the first equal similarity measure is equal to the second equal similarity measure; and an advertisement selection module configured to select advertisements for user sessions associated with a user identifier based on the symmetric association data. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22)
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Specification