Linkedin Corporation

Microsoft Technology Licensing, LLC

Utku Irmak

Samir Mavji Shah

Lawrence Yuan

Benjamin T Arai

Mark Hull

Ravi Kiran Holur Vijay

Ji Yan

Pramod Chand Khincha

Wynn Chen

Benjamin Lai

Ferris Jumah

Sean Seol Woong Choi

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

Techniques for predicting a user response to the e-mail content are described. According to various embodiments, member email interaction data associated with a particular member and email content data describing a particular email content item is accessed. The data is then encoded into one or more feature vectors and assembled to thereby generate an assembled feature vector. Thereafter, a prediction modeling process is performed, based on the assembled feature vector and a trained prediction model, to predict a likelihood of the particular member performing a particular user action on the particular email content item.

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Utku Irmak