Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, solving, for a set of input messages, a multi-objective optimization problem to minimize a number of messages to send while satisfying one or more constraints, selecting a random value for one or more message and message recipient pairs in the set of input messages, setting a send constraint for one or more of the pairs using a send threshold for the message in the set and the random value, and sending the message to a recipient for the message in the set in response to the send constraint for the pair being satisfied.

Abstract: This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, solving, for a set of input messages, a multi-objective optimization problem to minimize a number of messages to send while satisfying one or more constraints, selecting a random value for a message in the set, setting a send constraint for the message in the set using the send threshold for the message in the set and the random value, and sending the message in the set in response to the send constraint being satisfied.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for providing push notifications of social network content items include determining first and second engagement values for first and second content items, respectively, provided by first and second content item sources, respectively, based on previous interactions with content items. First and second utility values are determined for the first and second content items, respectively, based on a mathematical operation applied to the first and second engagement values, respectively, the first and second selection metrics, respectively, and, for the first utility value, the value metric. One of the first and second content items are determined as a push content item based on a difference between the first and second utility values and displayed on a user device.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for providing push notifications of social network content items include determining first and second engagement values for first and second content items, respectively, provided by first and second content item sources, respectively, based on previous interactions with content items. First and second utility values are determined for the first and second content items, respectively, based on a mathematical operation applied to the first and second engagement values, respectively, the first and second selection metrics, respectively, and, for the first utility value, the value metric. One of the first and second content items are determined as a push content item based on a difference between the first and second utility values and displayed on a user device.

Abstract: Systems and methods for providing push notifications of social network content items include determining first and second engagement values for first and second content items, respectively, provided by first and second content item sources, respectively, based on previous interactions with content items. First and second utility values are determined for the first and second content items, respectively, based on a mathematical operation applied to the first and second engagement values, respectively, the first and second selection metrics, respectively, and, for the first utility value, the value metric. One of the first and second content items are determined as a push content item based on a difference between the first and second utility values and displayed on a user device.

Abstract: 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.

Abstract: 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.

Abstract: 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.