Abstract: The present disclosure provides methods and systems for monitoring mental health of a subject. The methods and systems may comprise: (a) collecting data attributable to the subject at different time points; (b) providing the data to a computer system programmed with a machine learning algorithm, where the machine learning algorithm may process the data and may determine a status of mental health of the subject; and (c) providing the status of mental health of said subject to a recipient. The data may be derived from answers to inquiries provided on a plurality of requests individualized to the subject.

Abstract: 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.

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, 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 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.

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: The subject matter disclosed herein relates to ranking and/or indexing of documents based at least in part on user interaction with contextual shortcuts. In one or more implementations, data may be collected regarding user interaction with one or more contextual shortcuts and a query may be received from a user based at least in part on an activation of a given contextual shortcut. A search result may be transmitted to the user via a contextual search interface based at least in part on the collected data regarding user interaction with one or more contextual shortcuts.

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: During a targeting technique, features are extracted. Some features are associated with attributes in profiles of users of a social network (which facilitates interactions among the users), and others are associated with existing types of recommendations previously provided to the users in recommendations or otherwise associated with the users. Then, relevancy scores are determined based on the extracted features. Moreover, one or more of the extracted features are selected as rules for identifying a subset of types of recommendations to target at the users.

Abstract: A computer-implemented method for matching a display advertisement to a user within a large-scale, non-destructive user modeling and experimentation environment using real-time traffic. The method commences by populating a user profile object (containing demographics, history, and behaviors of the user) for use during concurrent operation of a production platform and an experimentation platform. To implement non-destructive testing, the method continues by cloning a portion of the real-time traffic for use by the experimentation platform while concurrently delivering the real-time traffic to the production platform. The production platform and the experimentation platform operate concurrently, scoring matches between the user profile objects and a plurality of display advertisements for selecting among the best-scored advertisements.

Abstract: Disclosed are methods and apparatus for performing named entity recognition. A set of candidates and corresponding contexts are obtained, each of the set of candidates being a potential seed example of an entity. The contexts of at least a portion of the set of candidates are compared with contexts of a set of seed examples of the entity such that a subset of the set of candidates are added to the set of the seed examples. A set of rules are created from the set of seed examples obtained in the comparing step. A final set of seed examples of the entity is generated by executing the set of rules against the set of candidates.

Abstract: Techniques are provided for ranking the entities that are identified in a document based on an estimated likelihood that a user will actually make use of the annotations. According to one disclosed approach, usage data that indicates how users interact with annotations contained in documents presented to the users is collected. Based on the usage data, weights are generated for features of a feature vector. The weights are then used to modify feature scores of entities, and the modified feature scores are used to determine how to annotate documents. Specifically, a set of entities are identified within a document. A ranking for the identified entities is determined based, at least in part, on (a) feature vector scores for each of the identified entities, and (b) the weights generated for the features of the feature vector. The document is then annotated based, at least in part, on the ranking.

Abstract: Disclosed are methods and apparatus for performing named entity recognition. A set of candidates and corresponding contexts are obtained, each of the set of candidates being a potential seed example of an entity. The contexts of at least a portion of the set of candidates are compared with contexts of a set of seed examples of the entity such that a subset of the set of candidates are added to the set of the seed examples. A set of rules are created from the set of seed examples obtained in the comparing step. A final set of seed examples of the entity is generated by executing the set of rules against the set of candidates.