Yahoo! Inc.

Excalibur Ip, LLC

George H John

Hongfeng Yin

Abhinav Gupta

Scott J Gaffney

Joshua M Koran

Pavel Berkhin

Long-ji Lin

Christina Yip Chung

John Canny

Chad Brower

Richard Frankel

Amitabh Seth

Shu-Yao Chien

Shi Zhong

Ankur Subhash Jain

Nikolai Rochnik

Abhinay Gupta

Shi Zhonog

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.

Behavioral targeting system

A current behavioral targeting system is first tested using a suite of test data. The output of the test is one or more performance metrics. Next, newly proposed behavioral targeting system created. The newly proposed behavioral targeting system is then evaluated using both the existing source data and a new source data. The evaluation of the newly proposed behavioral targeting system produces one or more performance metrics of the same type earlier calculated. Finally, the two sets of performance metrics are compared. The performance metric difference represents the impact of the new source data.

Methods of processing and segmenting web usage information

A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Granular data for behavioral targeting

Method and system for generating a linear machine learning model for predicting online user input actions

A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Granular data for behavioral targeting using predictive models

A method of determining content relevance for a user sets a user preference, which is related to a first area of content. The method calculates a set of scores, by using a combination, of the user preference, affinity data, and a parametric weight. The method organizes the content by using the set of scores, such that the organization of the content has a desirable relationship to the user, and recommends the selected content. Preferably, the method precomputes the affinity data and/or the parametric weight to generate and store the precompiled data for later retrieval. The affinity data describes a relationship between a first item of content and a second item of content, and the parametric weight describes an attribute of the second item. Additional embodiments include a system implementation and computer readable medium.

User interactive precision targeting principle

In an embodiment, new traffic pattern data is received pertaining to a first time period having a first time increment. The traffic pattern data may be received as, or converted to, count information reflecting the probabilities that the user will select particular links services. An incremental table is accessed to determine stale traffic pattern count data, which is the traffic pattern count data in the incremental table over a second time period having the first time increment, wherein the second time period covers the oldest traffic pattern count data in the incremental table. Then a count table is updated to reflect removal of the stale traffic pattern count data, wherein the count table contains global count data at a higher level of granularity than the incremental table. The incremental table and count table are updated to reflect addition of the new traffic pattern data.

Context-sensitive, self-adjusting targeting models

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Redwood City, CA, United States of America

George H John