Microsoft Technology Licensing, LLC

Romer E Rosales

Shaunak Chatterjee

Hsiao-Ping Tseng

Rupesh Gupta

Ravi Kiran Holur Vijay

Deepak Agarwal

Zheng Li

Bee-Chung Chen

Mohamed Gamal Mohamed Mahmoud

Xiaoyu Chen

Jinyun Yan

Joshua Daniel Hartman

Shubhanshu Nagar

Cameron Alexander Lee

Shilpa Gupta

Rishi Jobanputra

Ryan Oblak

Guanfeng Liang

Kwei-you Tao

A system and method for intermediate landing page rerouting are provided. In example embodiments, determine whether a webpage associated with a hyperlink has corresponding social network activities. Extract content from the webpage determined to have corresponding social network activities. In response to a selection of the hyperlink, reroute a web browser to an intermediate landing page. Cause presentation, at a user interface, of the extracted content and the corresponding social network activities.

Rerouting to an intermediate landing page

A system and method for content generation and targeting using machine learning are provided. In example embodiments, a probability that a user will visit a webpage based on historical data is calculated. A probability that the user will engage with a particular content category based on past user engagement is calculated. In response to the probability of the user engaging with the particular content category being equal to or greater than a first threshold, the content is generated. Further, in response to the probability of the user not visiting a webpage meeting or exceeding a second threshold, the generated content is sent to the user.

Content generation and targeting using machine learning

This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, the machine learning system to output an expected number of positive responses and an expected number of negative responses based on an input message, determining a threshold differential and a weight value using responses to the plurality of messages, and sending the input message in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being above the threshold differential.

Deterministic message distribution

This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages transmitted to target groups of an online social networking service, determining a threshold differential and a weight value using responses to the plurality of messages, and send the input message to the target in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being greater than the threshold differential.

Techniques for expanding a target audience for messaging

Techniques are provided for optimization of user interactions based on connection value scores. A method may comprise receiving input that indicates a selection of one or more social network activities to optimize for a particular member of a social network. The method may also comprise, based on the selection of the one or more social network activities, calculating a connection value score for a social network connection between the particular member and each user in a plurality of other users. Calculating the connection value score may comprise identifying one or more attribute values that are correlated with the one or more social network activities. Further, the method may comprise, based on the connection value score for each user of the plurality of other users, determining a ranking of the plurality of other users. The method may also comprise determining content based on the ranking of the plurality of other users.

Optimization of user interactions based on connection value scores

A method and apparatus for populating content items into a feed is provided. The feed comprises a sequence of content item ordered in such a way as to maximize a number of content items displayed to a user by virtue of the user scrolling down through the feed. The content items are each associated with a click-through rate, an indication of a number of times the content has been displayed to users, an indication of a number of times that the users have scrolled to a next item in the feed after the item was displayed, and a height of the content item. These values are used to train a behavioral model and then used by the behavioral model to layout the content items in a feed rendered at a user device.

Feeds by modelling scrolling behavior

This disclosure relates to systems and methods for managing multiple messages. In one example, a method includes determining a message transmission frequency threshold for a member of an online social networking service using responses from the member; receiving a message that is to be transmitted to the member; storing the message, without transmitting the message to the member, in a digest of messages for the member; and transmitting the digest to the member in response to a send score for the digest exceeding a send score threshold, the send score calculated using the number of messages in the digest.

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Burlingame, CA, United States of America

Romer E Rosales