Google Inc.

Nec Laboratories America, Inc.

Xiaodan Song

Yingwei Cui

Bahman Rabii

Jian Chen

Tao Huang

Fong Shen

Jeffrey Adgate Dean

Kai Chen

Kun Zhang

Gregory Sean Corrado

Rahul Sharma

Belle L Tseng

Xuerui Wang

Jie Han

Daniel Li

Quoc V Le

Yun Chi

Mingxing Tan

Golnaz Ghiasi

Tsung-Yi Lin

Pengchong Jin

Koji Hino

Xianzhi Du

Yin Cui

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing content are disclosed. In one aspect, a method includes storing, in a data structure, data specifying a future live event. An opportunity to provide the specified content to a user at a user device is identified. It is determined that (i) a time of the opportunity is between a start time and an end time for the live event, (ii) that a user device is located in a same geographic region as the live event based on geographical data provided by the user device, and (iii) the user is interested in the live event based on attributes of the user matching attributes of other users that were identified as interested in the live event (e.g., based on evaluation of online search data). The content is provided for display at the user device.

Event-based content distribution

Generating granular clusters for real-time processing is provided. The systems can identify tokens based on aggregating input from computing devices over a time interval. The systems can identify, based on metrics, a subset of tokens for cluster generation. The systems can generate, via a clustering technique, token clusters from the subset of the tokens, each of the token clusters comprising two or more tokens from the subset of the tokens. The systems can apply a de-duplication technique to each of the token clusters. The systems can apply a filtering technique to the token clusters to remove tokens erroneously grouped in a token cluster. The systems can assign, based on a selection process, a label for each of the token clusters. The systems can activate, based on a number of remaining tokens in each of the token clusters, a subset of the token clusters for real-time content selection.

Granular cluster generation for real-time processing

A computer-implemented method of generating scale-permuted models can generate models having improved accuracy and reduced evaluation computational requirements. The method can include defining, by a computing system including one or more computing devices, a search space including a plurality of candidate permutations of a plurality of candidate feature blocks, each of the plurality of candidate feature blocks having a respective scale. The method can include performing, by the computing system, a plurality of search iterations by a search algorithm to select a scale-permuted model from the search space, the scale-permuted model based at least in part on a candidate permutation of the plurality of candidate permutations.

Scale-permuted machine learning architecture

Machine learning based automatic audience segment in ad targeting

Systems and methods of providing information via a computer network are provided. A data processing system can identify a cluster that includes a plurality of online content items having a semantic or user similarity. The data processing system determines a plurality of cluster placement criteria of the cluster, and receives content configured for display with a web page. The content can be associated with the cluster based on the semantic or user similarity. A cluster placement criterion of the plurality of cluster placement criteria can be selected based on a quality metric of the selected cluster placement criterion, and the selected cluster placement criterion can be provided as a supplemental criterion used to select the content for display with the web page.

Content placement criteria expansion

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values to generate an alternative representation of the features of the resource, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input to generate a respective relevance score for each concept term in a pre-determined set of concept terms, wherein each of the respective relevance scores measures a predicted relevance of the corresponding concept term to the resource.

Scoring concept terms using a deep network

The present invention is directed generally to providing systems and methods for data analysis. More specifically, embodiments may provide system(s) and method(s) including dynamic user modeling techniques to capture the relational and dynamic patterns of information content and/or users' or entities' interests. Various embodiments may include system(s) and method(s) that are based on, for example, the past history of content semantics, temporal changes, and/or user community relationship. Various embodiments may include modeling and/or analysis of the dynamic nature of an item of interest's value to a user(s)/entity(ies) over time. The dynamic factors may be consider in any manner, such as, individually or combined, sequentially or simultaneously, etc. Further, some embodiments may include, for example, system(s) and method(s) relating to analyzing data to capture user/entity interests and/or characteristics, consider content semantics and evolutionary information, and/or using community relationships of users/entities to thereby analyze information and provide dynamic conclusion(s) (e.g., recommendation(s)).

Methods and systems for utilizing content, dynamic patterns, and/or relational information for data analysis

Systems and methods are disclosed for clusterizing information by determining similarity matrix for historical information and similarity matrix for current information; generating an aggregated similarity matrix (aggregated kernel); and applying evolutionary spectral clustering on the aggregated kernel to a content stream to produce one or more clusters.

Evolutionary spectral clustering by incorporating temporal smoothness

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San Jose, CA, United States of America

Xiaodan Song