Linkedin Corporation

Microsoft Technology Licensing, LLC

Lili Wu

Samir Mavji Shah

Manas S Khadilkar

Anmol Bhasin

Yang Zhou

Baoshi Yan

Yanen Li

Haishan Liu

Haifeng Ji

Liang Tang

Muhammad Aatif Awan

Peter B Rusev

Erica M Lockheimer

When a user of a social network accepts an invitation message to connect with another user of the social network, a system may use a history of user activity to determine where the user is subsequently directed within the social-network website. In particular, based on the history of user activity associated with an in-network page with recommendations for possible connections for the user within the network of users, the system may determine whether or not there have been too many impressions of the in-network page. If not, the system may present the in-network page with a recommendation for a possible in-network connection for the user within the network of users. Then, if the system receives a user selection of the possible in-network connection, the system may provide an invitation message inviting the possible in-network connection to connect with the user by activating a link in the invitation message.

User-specific and user-activity-based routing within a website

Systems and methods are disclosed that recommend one or more electronic presentations to a user based on one or more factors. These factors may include contextual information, behavioral information, profile information, or combinations of the foregoing. Contextual information may include content and/or features extracted from a given electronic presentation. Behavioral information may include user behavioral data, such as the number of times a user has viewed a presentation, the amount of the presentation viewed by the user, presentations previously viewed by the user, and other such behavioral data. Profile information may include user professional profile information, such as skills the user has identified as possessing, employment history information, and other such user professional profile information.

Providing recommendations for electronic presentations based on contextual and behavioral data

In an example embodiment, for each of a plurality of possible presented actions corresponding to a particular flow of actions in a computer network, and for each of one or more possible performed actions for each possible presented action, a likelihood that a user will perform the possible performed action is determined. Then, a first presented action is identified by determining a presented action having a maximum determined likelihood, based on the determined likelihood, wherein the identifying a first presented action includes utilizing a machine learning model having one or more user covariates and one or more performed action covariates, and interactions between the one or more user covariates and the one or more performed action covariates, the user covariates including information specific to the user, the one or more performed action covariates including information specific to one or more of the possible performed actions.

Site flow optimization

A method and system to present an optimum action in response to a flow of actions in a computer network from a user are provided. For each of a plurality of possible presented actions corresponding to a particular flow of actions in a computer network, and for each of one or more possible performed actions for each possible presented action, a likelihood that a user will perform the possible performed action is determined. Then each of the determined likelihoods is weighted by applying a weight assigned to a corresponding possible presented action. An optimum presented action is identified determining a presented action having a weighted maximum determined likelihood, based on the weighted determined likelihood.

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Palo Alto, CA, United States of America

Lili Wu