Verizon Media Inc.

Oath Inc.

Yahoo Ad Tech LLC

Gabriele Tolomei

Mounia Lalmas

Fabrizio Silvestri

Ayman Farahat

Nicola Barbieri

Michele Trevisiol

Puneet Mohan Sangal

Andy Haines

The present teaching relates to analyzing user behavior associated with web contents. Information related to user interactions associated with a content item placed on a reference property is first obtained. A measurement associated with each user interaction of the content item is determined based on the obtained information. An analyzing model for the content item which characterizes statistics of the measurements associated with the content item is further constructed. A measurement threshold to be used to determine a cost of placing the content item on a target property is further determined using the constructed analyzing model.

Method and system for analyzing user behavior associated with web contents

Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for changing a classification of a landing page, such as via, for example, identifying features of the landing page, such as to predict a binary classification of the landing page as to post-click user experience. One or more adjustments to features of the landing page may be determined, such as using a machine learning approach, by way of non-limiting example.

Changing machine learning classification of digital content

The present teaching relates to providing content supply adjustment. A first dataset associated with user interactions directed to one or more content items placed on a target property and a second dataset associated with user interactions directed to the one or more content items places on a reference property are received for evaluation. Further, a cost of placing the one or more content items on the target property is determined based on the first and second datasets.

Method and system for providing content supply adjustment

An online advertising system receives an advertisement from an advertiser. The system analyzes the advertisement, extracts its features and provides to the advertiser a quality rating for the advertisement which depends on a user engagement factor such as the predicted dwell time for the ad, given its features. The system further provides to the advertiser suggestions for improvements to the advertisement, such as a list of actionable guidelines that can improve the expected dwell time of the ad, and likely its conversion rate.

Growing community of inventors

London, United Kingdom

Gabriele Tolomei