Model for generating user profiles in a behavioral targeting system
First Claim
1. A method for determining user profiles from online activity, said method comprising:
- storing a plurality of weight parameters at an entity for a plurality of categories and a plurality of event types;
receiving, at said entity, event information, comprising an event type, from at least one event, wherein said event comprises on-line activity between said user and said entity;
classifying said event information in one of a plurality of categories, wherein a category specifies subject matter of user interest;
generating at least one user profile score for said category from said event information by;
selecting an intensity weight, based on said event type and said category, that measures an ability to predict intensity information for the corresponding event type and category;
applying a saturation function with a predefined upper cap value to said event information, wherein an output of said saturation function equals an input up to said upper cap value;
applying a decay function to said output of said saturation function, so as to decrease over time a predictive weight of said event information;
applying said intensity weight to an output of said decay function;
selecting a recency weight, based on said event type and said category, that defines a rate of decay for the prediction power of the corresponding event type and category;
applying said recency weight selected to the output of a recency function that measures how recent the event information occurred; and
aggregating the weighted output of said recency function with the weighted output of said decay function to generate said user profile score.
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Abstract
A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.
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Citations
18 Claims
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1. A method for determining user profiles from online activity, said method comprising:
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storing a plurality of weight parameters at an entity for a plurality of categories and a plurality of event types; receiving, at said entity, event information, comprising an event type, from at least one event, wherein said event comprises on-line activity between said user and said entity; classifying said event information in one of a plurality of categories, wherein a category specifies subject matter of user interest; generating at least one user profile score for said category from said event information by; selecting an intensity weight, based on said event type and said category, that measures an ability to predict intensity information for the corresponding event type and category; applying a saturation function with a predefined upper cap value to said event information, wherein an output of said saturation function equals an input up to said upper cap value; applying a decay function to said output of said saturation function, so as to decrease over time a predictive weight of said event information; applying said intensity weight to an output of said decay function; selecting a recency weight, based on said event type and said category, that defines a rate of decay for the prediction power of the corresponding event type and category; applying said recency weight selected to the output of a recency function that measures how recent the event information occurred; and aggregating the weighted output of said recency function with the weighted output of said decay function to generate said user profile score. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system for determining user profiles from online activity, said system comprising:
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storage for storing a plurality of weight parameters at an entity for a plurality of categories and a plurality of event types; at least one server computer, coupled to said storage, for receiving, at said entity, event information, comprising an event type, from at least one event, wherein said event comprises on-line activity between said user and said entity, for classifying said event information in one of a plurality of categories, wherein a category specifies subject matter of user interest, and for generating at least one user profile score for said category from said event information by; selecting an intensity weight based on said event type and said category, that measures an ability to predict intensity information for the corresponding event type and category; applying a saturation function with a predefined upper cap value to said event information, wherein an output of said saturation function equals an input up to said upper cap value; applying a decay function to said output of said saturation function, so as to decrease over time a predictive weight of said event information; applying said intensity weight to an output of said decay function; selecting a recency weight, based on said event type and said category, that defines a rate of decay for the prediction power of the corresponding event type and category; applying said recency weight selected to the output of a recency function, that measures how recent the event information occurred; and aggregating the weighted output of said recency function with the weighted output of said decay function to generate said user profile score. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A computer readable storage medium comprising a set of instructions which, when executed by a computer, cause the computer to determine user profiles from online activity, said instructions for:
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storing a plurality of weight parameters at an entity for a plurality of categories and a plurality of event types; receiving, at said entity, event information, comprising an event type, from at least one event, wherein said event comprises on-line activity between said user and said entity; classifying said event information in one of a plurality of categories, wherein a category specifies subject matter of user interest; generating at least one user profile score for said category from said event information by; selecting an intensity weight based on said event type and said category, that measures an ability to predict intensity information for the corresponding event type and category; applying a saturation function with a predefined upper cap value to said event information, wherein an output of said saturation function equals an input up to said upper cap value; applying a decay function to an output of said saturation function, so as to decrease over time a predictive weight of said event information; applying said intensity weight to an output of said decay function; selecting a recency weight, based on said event type and said category, that defines a rate of decay for the prediction power of the corresponding event type and category; applying said recency weight selected to the output of a recency function that measures how recent the event information occurred; and aggregating the weighted output of said recency function with the weighted output of said decay function to generate said user profile score - View Dependent Claims (14, 15, 16, 17, 18)
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Specification