Microsoft Technology Licensing, LLC

Linkedin Corporation

Lawrence Yuan

Utku Irmak

Mark Hull

Samir Mavji Shah

Benjamin T Arai

Ravi Kiran Holur Vijay

Ji Yan

Pramod Chand Khincha

Angela Yoonjeong Yang

Xiaowen Zhang

Chanh Nguyen

Wynn Chen

Benjamin Lai

Stephane Dahan

Stephen Philip Hiller

John Reilly

Bruno Pereira Medeiros

Vidya Chandrasekaran

Sean Seol Woong Choi

Alexandre J Lee

Techniques for improved categorization, processing, and display of electronic messages such as email are described. According to various embodiments, it is determined that a recipient corresponding to a member of an online social networking service has received an electronic message from a sender. Member profile data of the sender and the recipient is then accessed. Thereafter, a professional importance score associated with the message is calculated, based on the accessed member profile data of the sender and the recipient. The message is then classified into one of a plurality of professional importance categories, based on the determined professional importance score, and the message is marked in a message inbox user interface associated with the recipient, based on the classification.

Intelligent messaging

Methods and systems of providing a guided navigation experience are described. According to various embodiments, a first type of use case may be determined for a first use of a website by a user. The first type of use case may be determined amongst a plurality of types of use cases. A first navigation recommendation may be determined based on the determined first type of use case. The first navigation recommendation may comprise a recommendation for the user to navigate to a first content on the website. The first navigation recommendation may be caused to be displayed to the user. In some embodiments, the plurality of types of use cases may comprise at least one of viewing an update of a profile of another user, viewing an event associated with the profile of another user, performing a job search, and participating in or viewing a discussion.

Guided browsing experience

Techniques for onboarding users to various product features are described. According to various embodiments, information identifying a set of candidate onboarding content items associated with an email type of an email to be transmitted to a particular member of an online social network service is accessed. Thereafter, a first subset of the candidate onboarding content items is removed from the set, responsive to determining that the particular member has already been onboarded to products associated with the candidate onboarding content items in the first subset. Further, a second subset of the candidate onboarding content items is removed from the set, responsive to determining that the particular member has previously viewed and not interacted with the candidate onboarding content items in the second subset. A specific onboarding content item is then dynamically selected from the remaining set of candidate onboarding content items.

Product onboarding via messages

Techniques for onboarding users to online product features are described. According to various embodiments, email content data describing an email type of an email to be transmitted to a particular member is accessed. Information identifying a plurality of candidate onboarding content items associated with the email type is also accessed. Feature data including member onboarding content item interaction data describing the particular member's interactions with various onboarding content items is also accessed. Thereafter, for the particular member and the email type, a relevance score is generated for each of the candidate onboarding content items, based on the email content data and the member onboarding content item interaction data. Each of the relevance scores may indicate a likelihood that the particular member selects the corresponding candidate onboarding content item. A specific onboarding content item is then selected from the plurality of candidate onboarding content items, based on the generated relevance scores.

Personalized messages for product onboarding

Techniques for optimizing a delivery time for the delivery of messages are described. According to various embodiments, a system determines, for each of a plurality of time intervals, a likelihood of a particular member of an online social network service performing a particular member user action on a particular message content item during the corresponding time interval. The plurality of time intervals are then ranked, based on the determined likelihoods corresponding to the plurality of time intervals. Thereafter, a particular time interval is identified from among the plurality of time intervals that is associated with a highest ranking. The particular time interval is then classified as an optimum personalized message delivery time for the particular member.

Personalized delivery time optimization

Techniques for inferring a time zone of a user (e.g., a member of an online social network service) are described. According to various embodiments, it is determined that a particular member of an online social networks service is currently located in a particular country. Responsive to determining that a single time zone is associated with the particular country, it is determined that the particular member is currently located in the single time zone. Responsive to determining that multiple time zones are associated with the particular country, an IP address associated with a user login request is accessed, a geographic location associated with the IP address is identified, and a specific one of the multiple time zones that corresponds to the geographic location is identified. It is determined that the particular member is currently located in the specific time zone.

Member time zone inference

Techniques for optimizing a delivery time for the delivery of messages are described. According to various embodiments, members of an online social network service that are currently located in a particular geographic location (e.g., a particular time zone) are identified. Thereafter, messages (e.g., e-mails) are transmitted to the members at multiple local times (e.g., multiple times of the day). It is then determined that one or more of the messages that were transmitted at a particular local time have received a highest value for a response metric among the messages, the response metric indicating responses by the members to the messages. The particular local time is then classified as an optimum local message delivery time for the particular geographic location.

Delivery time optimization

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Campbell, CA, United States of America

Lawrence Yuan