Google Inc.

Roku, Inc.

Jing Xie

Jie Yang

Luis Garcia Pueyo

James Bradley Wendt

Ying Sheng

Marc Alexander Najork

Abhishek Bambha

Jinan Lou

Zidong Wang

Sandeep Tata

Yifeng Lu

Jing Lu

Qi Zhao

Rakesh Ravuru

Abbas Kazerouni

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for candidate ranking for content recommendation. An embodiment operates by receiving category candidates over a network, wherein each of the category candidates comprises content candidates associated with one or more applications operating on media devices. The embodiment then ranks the category candidates based on a machine model trained using a learning algorithm based on the time series data, and ranks the content candidates in the each of category candidates based on the time series data. The embodiment then causes the ranked category candidates and the ranked content candidates to be outputted for display.

Candidate ranking for content recommendation

Provided are computing systems and methods directed to active learning and may provide advantages or improvements to active learning applications for skewed data sets. A challenge in training and developing high-quality models for many supervised learning scenarios is obtaining labeled training examples. Provided are systems and methods for active learning on a training dataset that includes both labeled and unlabeled datapoints. In particular, the systems and methods described herein can select (e.g., at each of a number of iterations) a number of the unlabeled datapoints for which labels should be obtained to gain additional labeled datapoints on which to train a machine-learned model (e.g., machine-learned classifier model). Generally, provided are cost-effective methods and systems for selecting data to improve machine-learned models in applications such as the identification of content items in text, images, and/or audio.

Systems and methods for active learning

Techniques are described herein for automatically generating data extraction templates for structured documents (e.g., B2C emails, invoices, bills, invitations, etc.), and for assigning classifications to those data extraction templates to streamline data extraction from subsequent structured documents. In various implementations, a data extraction template generated from a cluster of structured documents that share fixed content may be identified. Features of the cluster of structured documents may be applied as input to extraction machine learning model(s) trained to provide location(s) of transient field(s) in structured documents, to determine location(s) of transient field(s) in the cluster of structured documents. An association between the data extraction template and the determined transient field location(s) may be stored. Based on the association, data point(s) may be extracted from a given structured document of a user that shares fixed content with the cluster of structured documents. The extracted data point(s) may be surfaced to the user.

Template-based structured document classification and extraction

Methods and apparatus are described herein for creating associations between user interests and electronic document templates generated from B2C electronic documents. Once these associations are created, interest(s) of a user (e.g., a user profile) may be determined automatically based on B2C electronic documents addressed to the user. In various implementations, an electronic document addressed to a user may be identified. A particular electronic document template that corresponds to the electronic document addressed to the user may be selected from a plurality of electronic document templates. The selecting may be based on attribute(s) shared between the electronic document addressed to the user and the selected electronic document template. The particular electronic template may be generated from a plurality of electronic documents that share fixed content. Interest(s) associated with the particular electronic document template may be identified, and association(s) between the user and the identified interest(s) may be stored.

Template-based identification of user interest

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San Jose, CA, United States of America

Jing Xie