Oath Inc.

Verizon Media Inc.

Yahoo Holdings, Inc.

Fabrizio Silvestri

Beverly L Harrison

Ricardo Alberto Baeza-Yates

Mounia Lalmas

Gabriele Tolomei

Di Jiang

Nicola Barbieri

Amin Mantrach

Puneet Mohan Sangal

Michele Trevisiol

Xiao Bai

Francesco Gullo

Andy Haines

B Barla Cambazoglu

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

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.

Generating actionable suggestions for improving user engagement with online advertisements

In one embodiment, a current context of a mobile device may be ascertained, where the current context includes an indication of a last application opened via the mobile device, wherein the last application opened is one of a plurality of applications installed on the mobile device. A probability, for each of the plurality of applications, that a user of the mobile device will use the corresponding application under the current context may be determined, where the probability for at least a portion of the plurality of applications is determined by applying a computer-generated model to the current context. One or more of the plurality of the applications may be identified based, at least in part, upon the probability, for each one of the plurality of applications, that the user of the mobile device will use the corresponding application.

Predicting the next application that you are going to use on aviate

In one embodiment, a current context of a mobile device may be ascertained, where the current context includes an indication of a last application opened via the mobile device, wherein the last application opened is one of a plurality of applications installed on the mobile device. A probability, for each of the plurality of applications, that a user of the mobile device will use the corresponding application under the current context may be determined, where the probability for at least a portion of the plurality of applications is determined by applying a computer-generated model to the current context, wherein the computer-generated model is associated with the mobile device. One or more of the plurality of the applications may be identified based, at least in part, upon the probability, for each one of the plurality of applications, that the user of the mobile device will use the corresponding application.

Software running on servers at a website hosting a news service generates a first profile for a user of the news service. The first profile is based at least in part on implicit relevance feedback from the user on content presented by the news service. The software obtains a second profile for the user from a web-searching service. The software creates a score for a candidate item of content. The score is based on similarity of the candidate item to the first profile and similarity of the candidate item to the second profile. Similarity to the second profile measures at least similarity to a plurality of web-search queries and similarity to any titles of any search results resulting from each of the queries. The software then presents the item of content to the user in a content stream served by the news service, based on the score.

Using exogenous sources for personalization of website services

Growing community of inventors

Barcelona, Spain

Fabrizio Silvestri