Abstract: A message spacing system evenly distributes the communication of one or more notifications to a computing device communicatively coupled with an online service. The message spacing system also instructs an application residing on the computing device to display a badge notification. The badge notification indicates a number of pending notifications awaiting review by a member of the online service. The badge notification may be overlaid an icon corresponding to an application that the member uses to access or interact with the online service. The badge notification may also be overlaid on an icon displayed on a webpage, where the icon represents a selectable topic that the member may select to interact with the online service. The notifications that the messaging spacing system may send include offline notifications and online notifications.

Abstract: Methods, systems, and computer programs are presented for scheduling user notifications to maximize short-term and long-term benefits from sending the notifications. One method includes an operation for identifying features of a state used for reinforcement learning. The state is associated with an action to decide if a notification to a user is to be sent and a reward for sending the notification to the user. Further, the method includes capturing user responses to notifications sent to users to obtain training data and training a machine-learning (ML) algorithm with reinforcement learning based on the features and the training data to obtain an ML model. Additionally, the method includes receiving a request to send a notification to the user, and deciding, by the ML model, whether to send the notification based on a current state. The notification is sent to the user based on the decision.

Abstract: In an example embodiment, a machine learned model is used to determine whether to send a notification for a feed object to a user. This machine learned model is optimized not just based on the likelihood that the notification will cause the user to interact with the feed object, but also the likely short-term and long-term impacts of the user interacting with the feed object. This machine learned model factors in not only the viewer's probability of immediate action, such as clicking on a feed object, but also the probability of long-term impact, such as the display causing the viewer to contribute content to the network or the viewer's response encouraging more people to contribute content to the network. As such, the machine learned model is optimized not just on notification interactivity but also on feed objects interactivity.

Abstract: Techniques for varying content item density are provided. A first minimum gap value is stored that dictates how close two content items of a first type may appear in a content item feed that contains content items of multiple types that includes the first type and a second type. The first minimum gap value is used to place content items in a first set of content item feeds. For each content item feed of the first set of content item feeds, performance data that indicates how well content items of the first type perform in the content item feed is generated. Based on the performance data and the first minimum gap value, a second minimum gap value that is different than the first minimum gap value is generated. The second minimum gap value is used to place content items in a second plurality of content item feeds.

Abstract: A unified notification platform for offline creation and distribution of notification content from a variety of data sources is described. The notification platform provides data adaptors that are reusable for generating notifications of different types, specifically, for generating notifications of different types that have features that have meaning across different notification types such that these features can be used to generate comparable relevance scores with respect to candidate profiles. The relevance score calculated for a notification with respect to a member profile is used to determine whether the notification is to be presented to the member represented by the member profile.

Abstract: A method can include determining, based on learned parameter values, an intrinsic interest and an affinity for the user to be influenced to visit the website, determining, using the learned parameter values, intrinsic interest, and affinity for the user to be influenced to visit the website, a first probability indicating a likelihood that the user will, in response to viewing a badge notification, turn off notifications or delete an app and a second probability indicating a likelihood that the user will, in response to viewing the badge notification on the app, visit a website, in response to determining the second probability is greater than a threshold larger than the first probability, causing the app to include the badge notification when displayed on the user device.

Abstract: Technologies for optimizing content delivery to end-users are provided. Disclosed techniques include storing results of an online experiment with respect to a set of users and determining a plurality of distinct subsets of users based upon the results of the experiment. Users within each of the plurality of distinct subsets may be identified based on metric impacts of the online experiment. For each distinct subset and each associated model parameter, a utility value that represents effectiveness of the model parameter, with respect to an objective, may be determined. An objective optimization model may be used to automatically determine probabilities for each of the model parameters associated with each distinct subset. Users of a second set of users may be assigned to a distinct subset and associated model parameters may be applied to a content delivery strategies of the second set of users.

Abstract: Techniques for automatically identifying a primary objective for a multi-objective optimization problem are provided. In one technique, an experiment is conduct and results of the experiment involving different values of a model parameter are tracked and stored. Multiple metrics are generated based on the results. For each metric, a maximum or minimum value of the metric given a particular value of the model parameter is determined and a variance associated with the metric is determined based on the maximum or minimum value. A metric that is associated with the lowest variance among the multiple metrics is identified. The identified metric is used as a primary metric in a multi-objective optimization problem.

Abstract: A machine for content-feedback-based machine learning to incent online content creation. The machine accesses a relevance value that identifies a level of relevance of a content item to a user. The content item is created by a content creator. The machine generates, using a machine learning model, a feedback sensitivity score associated with the content creator. The machine generates, based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item, a ranking score for the content item. The machine causes display of the content item, based on the ranking score, in a user interface of a client device associated with the user. An input pertaining to the content item received via the user interface causes improvement of the machine learning model based on updating the one or more feedback features.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Future Connection Engine that generates a select pairing of member accounts for a potential social network connection. The Future Connection Engine predicts, according to the prediction model, a first number of subsequent social network connections for a first member account in the select pairing that will occur after establishing the potential social network connection and a second number of subsequent social network connections for a second member account in the select pairing that will occur after establishing the potential social network connection. The Future Connection Engine generates connection recommendations for display to the select pairing based on whether the first and/or the second number of subsequent social network connections satisfies a threshold.

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: A computer-implemented method may determine content items regarding a subject to be high demand and sufficient supply, low demand and supply constrained, high demand and supply constrained, or low demand and supply constrained. The computer-implemented method may determine the following: a supply and demand of content items regarding a subject for members, supply demand ratios for the content items regarding the subject for each of the plurality of members, a median supply demand ratio of the supply demand ratios, a total demand for the content items regarding the subject, a median total demand of total demands for the content items regarding subjects for the members, and a median of median supplies demand ratios for the content items regarding the subjects for the members. The method may perform steps to improve demand or supply of a connection network.

Abstract: A machine for content-feedback-based machine learning to incent online content creation. The machine accesses a relevance value that identifies a level of relevance of a content item to a user. The content item is created by a content creator. The machine generates, using a machine learning model, a feedback sensitivity score associated with the content creator. The machine generates, based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item, a ranking score for the content item. The machine causes display of the content item, based on the ranking score, in a user interface of a client device associated with the user. An input pertaining to the content item received via the user interface causes improvement of the machine learning model based on updating the one or more feedback features.

Abstract: Systems and techniques for content creator messaging framework are described herein. Information that indicates member activities corresponding to a content item corresponding to a content segment may be obtained for a date range. A set of distinct members may be determined that are associated with the information that indicates member activities. Edges may be identified in a connections network between each member of the set of distinct members and the content creator. An edge weight may be calculated for each edge using a number of interactions between content items created by the content creator and the member. A content creator ranking may be generated for the content creator using the edge weight for each edge. A content creator notification may be transmitted to the content creator based on determining that the content creator ranking is outside a threshold.

Abstract: In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.

Abstract: Techniques for automatically merging multiple content item queues are provided. In one technique, a first set of content items of a first type is identified. A second set of content items of a second type that is different than first type is identified. The first set of content items and the second set of content items are merged in a content item feed. Such merging involves, for a particular slot in the content item feed: determining a previous slot that contains a first content item from the first set; determining a number of slots between the previous slot and the particular slot; based on the number of slots, generating a score for a second content item from the second set; and based on the score, determining whether to insert, into the particular slot, the second content item or a third content item from the first set of content items.

Abstract: Techniques for minimizing variance in the estimation of the effects of a treatment on an online network are disclosed herein.

Abstract: In an example embodiment, a machine learned model is used to determine whether to send a notification for a feed object to a user. This machine learned model is optimized not just based on the likelihood that the notification will cause the user to interact with the feed object, but also the likely short-term and long-term impacts of the user interacting with the feed object. This machine learned model factors in not only the viewer's probability of immediate action, such as clicking on a feed object, but also the probability of long-term impact, such as the display causing the viewer to contribute content to the network or the viewer's response encouraging more people to contribute content to the network. As such, the machine learned model is optimized not just on notification interactivity but also on feed objects interactivity.

Abstract: Techniques for automatically merging multiple content item queues are provided. In one technique, a first set of content items of a first type is identified. A second set of content items of a second type that is different than first type is identified. The the first set of content items and the second set of content items are merged in a content item feed. Such merging involves, for a particular slot in the content item feed: determining a previous slot that contains a first content item from the first set; determining a number of slots between the previous slot and the particular slot; based on the number of slots, generating a score for a second content item from the second set; and based on the score, determining whether to insert, into the particular slot, the second content item or a third content item from the first set of content items.

Abstract: Techniques for varying content item density are provided. A first minimum gap value is stored that dictates how close two content items of a first type may appear in a content item feed that contains content items of multiple types that includes the first type and a second type. The first minimum gap value is used to place content items in a first set of content item feeds. For each content item feed of the first set of content item feeds, performance data that indicates how well content items of the first type perform in the content item feed is generated. Based on the performance data and the first minimum gap value, a second minimum gap value that is different than the first minimum gap value is generated. The second minimum gap value is used to place content items in a second plurality of content item feeds.