Google Inc.

Yifang Liu

Zhenyu Liu

Konstantinos Katsiapis

Christopher Kenneth Harris

Roberto Javier Bayardo

Mayur Dhondu Datar

Ping Wu

Clemens Buehling

Allen Pin-Hsiu Huang

Samuel Ieong

Fei Ye

Jing Li

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing content items based on a location of a user's attention in a map interface. In one aspect, a method includes receiving data specifying one or more map attention spots in a viewport of a map interface presented at a user device. Each map attention spot is a location on a map of the map interface corresponding to a specified amount of user activity. Content items are identified for presentation with the map. For each content item and map attention spot, a distance between a presentation location for the content item on the map and a location of the identified map attention spot on the map is determined. A rank score for the content item based at least on a respective content item score for the content item and each determined distance for the content item.

Attention spots in a map interface

When a content item is initially served to a client device, the content item may result in an impression effect. As time elapses, the initial impression may fade. Such a decay of the impression effect may be predicted through the use of a predictive model. In some implementations, one or more impression effect parameters may be accessed and used with the predictive model to determine a decay factor or predicted value that incorporates the impression effect decay for a content item. A value, such as a score, may be determined based on the decay factor or the predicted value and a bid associated with a content item. A content item may be selected based on the determined value and data to effect presentation of the content item may be provided.

Content item impression effect decay

Systems and techniques are provided for determining mutual information with absolute dependency for feature selection. Items may be received from a dataset. Each item may include two random variables. A first random variable may be associated with a first range of discrete values, and a second random variable may be associated with a second range of discrete values. Mutual information between the two random variables may be determined according to one of: I(X,Y) may be the mutual information between X and Y, x may be a value for X, y may be a value for Y, p(x,y) may be a joint probability distribution function of x and y, p(x) may be a marginal probability distribution function of x, and p(y) may be a marginal probability distribution function of y. The mutual information may be used in a machine learning system to predict a value for one of the random variables for an item for which the value is unknown.

Mutual information with absolute dependency for feature selection in machine learning models

The modeling of an impression effect may include generating a content item impression effect distribution. A classification model may be used to determine a period of the content item impression effect distribution based on one or more accessed impression effect parameters. A value for a content item may be determined based, at least in part, on the determined period and a bid associated with the content item. A content item may be selected based on the determined value and data to display the selected content item may be transmitted. In some instances, the determined period may be used to determine or select predictive model for the determined period that outputs a factor to modify the determined value.

Impression effect modeling for content items

Systems, methods, routines and/or techniques for a local mobile memo for non-interrupting link noting are described. One or more embodiments may include a method that may include displaying to a user a page containing a link, receiving a first user input that indicates the link should be saved for potential later processing and saving the link to a local repository so that the link can be processed later. The saving may occur while the page continues to be displayed. The saving may occur without processing the link to perform the associated action. The method may include displaying a list (e.g., including the link) of one or more links saved in the repository, for example, in response to an event that indicates that the user may be interested in viewing links in the link repository.

Local mobile memo for non-interrupting link noting

Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium for determining keywords. A method includes determining keyword(s) from a set of event information. The set can include plural, similar events so as to enable an effective analysis of a group of events as opposed to an analysis of a single discrete event. The set of events can be formed by filtering a corpus of events by a category. The corpus of events can include, for each event, information such as a search query, a web page associated with the query or visited by a user device, a location associated with a user device, or a search result response. Top ranked terms can be determined based on term frequency. Keyword(s) can be determined for each top ranked term. The keyword(s) can be used to select content for delivery to user devices associated with the category.

Frequency-based content analysis

A system and method for social-aware clustering of user data replicas in a large-scale distributed computing system is disclosed. An exemplary system finds at least one user's connected users based on communications between the user and other users. The datacenters that contain the user replicas of the user's connected users are found. Connections and connection weights between the user and the user's connected users' datacenters are computed. The preferred datacenters for the user's current user data replica is computed based on the location of the connected datacenters and the weights of the connections. An optimization model minimizes the distance between the user's current datacenter and the user's preferred datacenter to reduce network traffic and central processing unit usage and determines the user's datacenter. The user's current datacenter is updated to the datacenter determined by running the optimization model.

Social-aware resource allocation for large-scale distributed systems

Multi-class classification by training a machine learning system based on training inputs each of which includes features and at least one class label. Each training input is assigned a membership value that can indicate if an entity having the features of the training input is a member of the class corresponding to the class label that is also included in the training input. To determine if an entity having test features is a member of several test classes, test inputs can be constructed where each input includes the test features and a class label corresponding to one of the test classes. The test inputs are processed by the trained machine learning system, which produces as outputs test membership values that represent the likelihood that the entity having the features in the test input belong to the test class corresponding to the test class label also included in the test input.

Classification system

A method for enabling a user of a computing device to designate a content item for subsequent review is provided. The computing device is coupled to a memory device and a display. The method is implemented using the computing device. The method includes displaying, by the computing device, a first publication. The method further includes the steps of displaying, by the computing device, a content item at a first time, wherein the content item is displayed in conjunction with the first publication. The method further includes receiving, by the computing device, a first input from the user to designate the content item for subsequent review, generating an indicator that the content item has been designated for subsequent review, causing the memory device to store the indicator, determining a second time to display the content item, and displaying the content item at the second time.

Presentation of non-interrupting content items

Provided are methods and systems for constructing a personal knowledge graph for a user based on data contained in existing e-mail messages of the user, and using the personal knowledge graph to provide the user with contextually-relevant content and/or contact suggestions while the user is composing an e-mail message. A personal knowledge graph is constructed based on relations/connections between users and content identified from data contained in e-mail messages sent and/or received by the user. Such relations include content-content relations, user-content relations, and user-(content)-user relations. When a user is composing an e-mail message, the system responsively processes, analyzes, and indexes composing e-mail message data. The composing e-mail message data is used to fetch relevant information from the user's personal knowledge graph and generate one or more content and/or contact suggestions for presentation to the user alongside an e-mail message composing view.

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Redwood City, CA, United States of America

Yifang Liu