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 measuring user engagement with online products and content are described. According to various embodiments, user sequence flow information is generated for a given member, based on online session log data, the user sequence flow information indicating a chronological sequence of steps and associated activities, each of the activities being any one of a product page view activity and a user action activity. The generation of the user sequence flow information may comprise grouping one or more sequential page views of a product described in the online session log data for the member into a product page view activity in the user sequence flow information, and grouping one or more sequential user actions performed in conjunction with a product described in the online session log data for the member into a user action activity in the user sequence flow information.

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, 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: 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.

Abstract: Techniques for providing information describing how users are funneling through various products and features of a website are described. According to various embodiments, a user specification of a first set of one or more entities is received. A data structure storing a plurality of data structure entities is then accessed, each of the data structure entities corresponding to an online user session and describing one or more user interaction events included in the corresponding online user session. A set of the plurality of data structure entities are then retrieved from the data structure, the set of the plurality of data structure entities corresponding to online user sessions that include a user interaction event with at least one of the entities in the first set. Information regarding the retrieved set of the plurality of data structure entities is then provided to a user, via a user interface.

Abstract: Techniques for providing information describing how users are funneling through various products and features of a website are described. According to various embodiments, a user specification of a first set of one or more entities is received. A user interface element is then displayed, the user interface element indicating an amount of total online user sessions that include user interactions with at least one of the entities in the first set during a specific time interval. Thereafter, a user specification of a second set of one or more entities is received. A second user interface element is then displayed, the second user interface element indicating a subset of the total online user sessions that include user interactions with at least one of the entities in the second set subsequent to the user interactions with at least one of the entities in the first set.

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 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: 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 measuring user engagement with online products and content are described. According to various embodiments, user sequence flow information is generated for a given member, based on online session log data, the user sequence flow information indicating a chronological sequence of steps and associated activities, each of the activities being any one of a product page view activity and a user action activity. The generation of the user sequence flow information may comprise grouping one or more sequential page views of a product described in the online session log data for the member into a product page view activity in the user sequence flow information, and grouping one or more sequential user actions performed in conjunction with a product described in the online session log data for the member into a user action activity in the user sequence flow information.

Abstract: 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.

Abstract: A URL reputation system may have a reputation server and a client device with a cache of reputation information. A URL reputation query from the client to the server may return reputation data along with probabilistic set membership information for several variants of the requested URL. The client may use the probabilistic set membership information to determine if the reputation server has additional information for another related URL as well as whether the classifications are inheritable from one of the variants. If the probabilistic set membership determines that the reputation server may have additional information, a query may be made to the reputation server, otherwise the reputation may be inferred from the data stored in the cache.

Abstract: A URL reputation system may have a reputation server and a client device with a cache of reputation information. A URL reputation query from the client to the server may return reputation data along with probabilistic set membership information for several variants of the requested URL. The client may use the probabilistic set membership information to determine if the reputation server has additional information for another related URL as well as whether the classifications are inheritable from one of the variants. If the probabilistic set membership determines that the reputation server may have additional information, a query may be made to the reputation server, otherwise the reputation may be inferred from the data stored in the cache.