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 device, system, and method determine, for each of a first group of content item sources of a social networking system, an output quality metric based on activity data accessed from an electronic data storage related to content items previously output by a respective content item source. Each of the content item sources are configured to output a ranked list of content items stored in the electronic data storage according to a ranking schema different for each of the content item sources. An integrated ranked list is generated based on the ranked lists of content items from each of the content item sources and the output quality metric of the respective content item sources. A content item stream is generated based on the integrated ranked list and ranked lists from each of a second group of content item sources different from the first group of content item sources.

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: Disclosed in some examples, are systems, methods, and machine readable mediums which implement a scalable algorithm for scheduling promotional campaigns of an online service that satisfy a set of desired constraints while at the same time maximizing a total utility. This algorithm is capable of scheduling hundreds of campaigns for millions of members. In some examples, each promotional campaign may have a utility (which may be described by a utility function) for a particular member and a goal of the scheduling algorithm may be to maximize the total utility for all members eligible for the promotional campaign while satisfying various constraints.