Google Inc.

Twitter, Inc.

Jeremy Ginsberg

Simon Tong

David Braginsky

Parag Agrawal

Michael Jahr

Sandeep Pandey

Yue Lu

Cheng Li

In some examples, a computing device includes at least one processor and at least one module, operable by the at least one processor to receive, from a client device of a user, a request for one or more advertisements to display at the client device with a set of messages. The set of messages is associated with the user in a social network messaging service. The at least one module may be further operable to determine a probability that the user will select a candidate advertisement using a machine learning model based on point-wise learning and pair-wise learning. The at least one module may be further operable to determine, based on the probability that the user will select the candidate advertisement, a candidate score for the candidate advertisement, determine that the candidate score satisfies a threshold, and send, for display at the client device, the candidate advertisement.

Click-through prediction for targeted content

A system receives a search query from a user and searches a group of repositories, based on the search query, to identify, for each of the repositories, a set of search results. The system also identifies one of the repositories based on a likelihood that the user desires information from the identified repository and presents the set of search results associated with the identified repository.

Determination of a desired repository for retrieving search results

Determination of a desired repository

A system receives a search query from a user and searches a group of repositories, based on the search query, to identify, for each of the repositories, a set of search results. The system also identifies one of the repositories based on a likelihood that the user desires information from the identified repository and presents the set of search results associated with the identifies repository.

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San Francisco, CA, United States of America

Jeremy Ginsberg