Google Inc.

Deepak Ravichandran

Yijian Bai

Shankar R Ponnekanti

Jia Liu

Sittichai Jiampojamarn

Manojav Patil

Gang Wang

Shumeet Baluja

Rohan Seth

Dandapani Sivakumar

Michael Kleber

Zhenyu Liu

Nitish John Korula

Josh Karlin

Jason Hsueh

The present disclosure provides systems and methods for client-side anonymized content selections. The method includes collecting a plurality of identifications of content selection lists, each content selection list associated with the first device and at least one other device. The method includes selecting a first subset of the content selection lists, responsive to a total number of unique device associated with a plurality of content selection lists of the first subset exceeding a threshold. The method includes transmitting a request for an item of content, the request comprising identifications of the content selection lists of the selected first subset. The method includes receiving a first item of content selected by the content server based on the content selection lists of the selected first subset.

Systems and methods for preserving device privacy

Privacy preserving remarketing

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting local search queries. In one aspect, a method includes accessing a search query log that includes data specifying search queries corresponding to particular geographic regions and for at least one of the search queries corresponding to the particular geographic region generating a geo-query count that represents a total number of times that the search query was received over a specified period. The geo-query count is compared to a corresponding expected query count for the search query, where the expected query count is a baseline number of times that the query is expected to be received. In response to determining that the search query has a geo-query count that exceeds the corresponding expected query count by at least a threshold amount, the particular query is classified as a local query for the particular geographic region.

Geographically local query detection

A computerized method and system operable to build a device identifier similarity model with online event signals and determine similar network device identifiers. A processing circuit receives a first set of network device identifiers. The processing circuit represents each network device identifier of the first set by feature data associated with each network device identifier's network activity, where the feature data is associated with the content clicked-on or converted-on. The processing circuit applies abstractions on the feature data to form concepts. The processing circuit derives at least one hierarchy of feature data based on the keywords and concepts of the feature data. The processing circuit expands the feature data based on the derived at least one hierarchy of feature data and generates the device identifier similarity model based on the expanded feature data. The processing circuit is also capable of determining long-term and short-term history events.

Device identifier similarity models derived from online event signals

A computerized method for generating a representative model for a plurality of different models identified by similar feature data. A processing circuit receives a plurality of different models, each model configured for use in generating a second set of network user identifiers based on a first set of network user identifiers. The processing circuit receives feature data for each of the plurality of different models, each feature data having a corresponding feature weight data. The processing circuit identifies similar models within the plurality of different models based on a similarity of the feature data between models within the plurality of different models. The processing circuit generates the representative model to represent the similar models. The representative model may be used to generate the second set of network user identifiers based on the feature data and corresponding weight data of the representative model.

Generating a representative model for a plurality of models identified by similar feature data

A computerized method of creating a classifier model to determine whether a network user should be added to a list of similar network users. A first list of network users, a second list of network users who are not on the first list of network users and characteristic data associated with the network users' network activity are received. A time period is designated. A first category of network activities performed by a network user on the first list within the designated time period is identified. A second category of network activities performed at any time by network users on the second list is identified. A third category of network activities performed by network users on the first list at a time outside of the designated time period is identified. The classifier model is generated based on the first, second and third categories of network activities.

Creating a classifier model to determine whether a network user should be added to a list

A computerized method, system for, and computer-readable medium operable to select an algorithm for generating models configured to identify similar user identifiers. A first plurality of models generated by a first algorithm is received. A plurality of lists of similar user identifiers is generated. User queries associated with user identifiers on the plurality of lists of similar user identifiers are identified. Predicted click-through rates for the user queries is received. An average predicted click-through rate is computed for each model based on the predicted click-through rates. A weighted average predicted click-through rate associated with the first plurality of models is computed. The weighted average predicted click-through rate for the first plurality of models can be compared to a weighted average predicted click-through rate for a second plurality of models generated by a second algorithm. The algorithm for generating models is selected based on the comparison.

Selecting an algorithm for identifying similar user identifiers based on predicted click-through-rate

A method of setting a threshold similarity score value for a first plurality of network user identifiers. The first plurality of network user identifiers, a second plurality of network user identifiers and characteristic data associated with the network user identifiers is received. A performance target and an experimental threshold similarity score value are designated. A similarity score between the first and second plurality of network user identifiers is calculated. Performance statistics data for each of the second plurality of network user identifiers having a similarity score greater than or equal to the experimental threshold similarity score value is collected and compared to the similarity score of the network user identifier. Based on the comparison, the experimental threshold similarity score value is adjusted to a similarity score value that achieves the performance target and the threshold similarity score value is set to the adjusted experimental threshold similarity score value.

Adjust similar users identification based on performance feedback

Systems and methods of selecting consumers to receive content on a computer network are provided. A user list identifying a first plurality of users having a group of features corresponding to internet activity of the first plurality of users can be obtained at a computing device. A subgroup of features can be selected from the group of features, and a cluster of users of the first plurality of users can be identified. The users of the cluster of users can each have at least one feature of the subgroup of features. A supplemental user having a supplemental feature can be identified. A correlation between the supplemental feature and at least one feature of the subgroup of features can be determined, and an expanded user list that includes at least one of the first plurality of users and the supplemental user can be generated.

Computer networked selecting of consumer interests

A computerized method for determining a model refresh rate for a model representing a list of network user identifiers includes receiving a first and a second model representing the list at a first and a second time, respectively. Similarity between the first and second model is calculated. If the similarity is less than a threshold value, the model refresh rate is a predetermined rate. If the similarity is equal to or exceeds the threshold value, the model refresh rate is decreased to a less frequent rate. A similarity between a reference model and a selected model in a same content category may be calculated. If the similarity is less than a threshold value, the model refresh rate for the selected model is a predetermined rate. If the similarity is equal to or exceeds the threshold value, the model refresh rate for the reference model is applied to the selected model.

Determining a model refresh rate

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting keywords for resources. In one aspect, a method includes identifying a particular online resource that includes non-text content. Co-visitation data are obtained for the particular resource. The co-visitation data specify one or more co-requested online resources for the particular online resource. Each of the co-requested online resources were requested by a user device within a threshold period of the request for the particular online resource by the user device. Keywords are identified for each of the co-requested online resources, and can include keywords that were selected based on text content of the co-requested online resource. One or more of the identified keywords are selected as keywords for the particular resource.

Selecting keywords using co-visitation information

A computerized method, system for, and computer-readable medium operable to select a list of network user identifiers. A processing circuit receives a list of network user identifiers represented by long-term history data indicative of web pages visited prior to a first time and short-term history data indicative of web pages visited after the first time and prior to a second time. Long-term interest categories, corresponding weight values for each long-term interest category, short-term interest categories and corresponding weight values for each short-term interest category are identified. A model comprising the long-term and short-term interest categories is generated based on the weight values of the long-term and short-term interest categories using either arithmetic or harmonic progression. The processing circuit receives a list of candidate network user identifiers and generates a list of similar network user identifiers based on the model.

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Mountain View, CA, United States of America

Deepak Ravichandran