Oath Inc.

Yahoo! Inc.

Michele Trevisiol

Alejandro Jaimes

Luca Maria Aiello

Luca Chiarandini

Nicola Barbieri

Mounia Lalmas

Fabrizio Silvestri

Gabriele Tolomei

Puneet Mohan Sangal

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

A method for tailoring content in a web page is provided. There is a relationship between the source domain a user comes from and the behavior pattern of a user on a website. To predict the behavior patterns of a particular user coming from a particular source domain, first a large dataset is compiled from user logs. Second, session analysis is performed on the dataset to identify sessions, session characteristics, page view categories, and source categories. Third, sessions are clustered and analyzed to determine how the behavior changes according, to a source category. Fourth, a mapping between source categories and behavior patterns is generated. When a user requests a page view from a source domain, if the source domain is mapped to a source category, then a tailored version of the page view is presented to the user based on the mapping between source categories and behavior patterns.

User behavior models based on source domain

A method for tailoring content in a web page is provided. There is a relationship between the source domain a user comes from and the behavior pattern of a user on a website. To predict the behavior patterns of a particular user coming from a particular source domain, first a large dataset is compiled from user logs. Second, session analysis is performed on the dataset to identify sessions, session characteristics, page view categories, and source categories. Third, sessions are clustered and analyzed to determine how the behavior changes according to a source category. Fourth, a mapping between source categories and behavior patterns is generated. When a user requests a page view from a source domain, if the source domain is mapped to a source category, then a tailored version of the page view is presented to the user based on the mapping between source categories and behavior patterns.

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Barcelona, Spain

Michele Trevisiol