Yahoo Assets LLC

Verizon Media Inc.

Oath Inc.

Yahoo! Inc.

Yahoo Ad Tech LLC

Suleyman Cetintas

Jian Yang

Kuang-chih Lee

Xian Wu

Jia Li

Ben M Shahshahani

Srinath Ravindran

Mohammad Saberian

Puneet Mohan Sangal

Pengyuan Wang

Pradhan Pattanayak

Sandeep Soni

Hong Yao

Pengyang Wang

The present teaching relates to method, system, medium, and implementations for machine learning. Upon receiving input data associated with a time series, hidden representations associated with the time series in a feature space are obtained and used to generate a query vector in a query space. Such generated query vector is then used to query relevant historic information related to the time series. The query vector and the relevant historic information are aggregated to generate at least one queried vector, which is aggregated with the hidden representations to generate enriched hidden representations that enhance the expressiveness of the hidden representations.

System and method for deep enriched neural networks for time series forecasting

Disclosed are systems and methods utilizing neural contextual bandit for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to make item recommendations using latent relations and latent representations, which can improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods use neural network modeling in automatic selection of a number of items for recommendation to a user and using feedback in connection with the recommendation for further training of the model(s).

Neural contextual bandit based computational recommendation method and apparatus

A method includes executing operations to generate a first enhancement function based on a parent-child link in a content hierarchy including a link between a parent node in a first level of the content hierarchy to a child node in a second level of the content hierarchy below the first level. A second enhancement function is generated based on a sibling link in the content hierarchy including a link between a sibling node in a third level of the content hierarchy and a sibling node in the third level of the content hierarchy sharing a common parent node with the first sibling node in a fourth level of the content hierarchy above the third level. A user content consumption metric is generated based on the first and second enhancement functions. A content list including a set of candidate content items ranked based on the user content consumption metric is generated.

Hierarchy aware graph representation learning

Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.

Generating preference indices for image content

Systems, devices, and methods are disclosed for predicting potential effectiveness of query-triggered internet advertisements received from different web page publishers using a deep learning neural network language model for clustering queries, and for automatically adjusting bids for advertisements by advertisers based on the predicted potential effectiveness. Using query-clusters rather than queries for adjusting bids for advertisements allows for more accurate and more consistent bidding strategy despite of sparsity in historical advertisement performance data, higher return on investments for the advertisers, and higher revenue for the publishers of the advertisements.

System and method for automated bidding using deep neural language models

Users may consume and/or share information through various types of content items. For example, user may post a family photo through a social network, create a running blog through a microblogging service, etc. Because users may be overwhelmed by the amount of available content items, it may be advantageous to recommend content items, such as blogs to follow, to users. Accordingly, inductive matrix completion is used to evaluate user interactions with content items (e.g., a user following a blog), content item features (e.g., text and/or images of a blog is evaluated to identify a topic of the blog), and/or user features (e.g., a user liking or reblogging a blog, user demographics, user interests, etc.) to determine whether to recommend a content item to a user. Additionally, graph proximity is used to recommend content items based upon weights of edges connecting user nodes to content item nodes within a directed graph.

Inductive matrix completion and graph proximity for content item recommendation

The technologies described herein serve contextually relevant advertisements under a guaranteed advertisement campaign. A publisher retrieves a guaranteed advertisement campaign related to a webpage available for serving an advertisement, and identifies a set of advertisements relating to the guaranteed advertisement campaign. Advertisement selecting circuitry of the publisher determines whether an advertisement that is contextually relevant to content published at the webpage is present in the set of advertisements. If there is no contextually relevant advertisement in the set of advertisements, the advertisement selecting circuitry selects an alternative advertisement from the set of advertisements that minimizes an under-delivery risk related to the guaranteed advertisement campaign. If there is a contextually relevant advertisement in the set of advertisements, the advertisement selecting circuitry selects the contextually relevant advertisement. Then, the publisher provides the selected advertisement to a client device.

System and method for contextual video advertisement serving in guaranteed display advertising

Systems, devices, and methods are disclosed for determining the quality of traffic received from different web page publishers, and setting a pricing structure for the different traffic based on the determined quality of traffic. Accurately determining the quality of traffic and/or clicks from different publishers allows the network system described herein to offer a fair marketplace with just return on investments (ROI) for advertisers, and offer a robust and accurate traffic quality based pricing model for publishers. Internet based technology, and in particular deep learning techniques available through a neural network, are utilized to determine the pricing structure based on click and/or web page traffic quality measurements generated through the deep learning techniques.

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Santa Clara, CA, United States of America

Suleyman Cetintas