Abstract: Systems and methods are directed to providing multilevel chained testing. A modeling manager receives a request for data associated with an experience having multiple levels of testing, whereby each lower level of testing has a set of one or more variants chained to a variant of a higher level. Based on the request, the model manager determines which variant of the multiple levels of testing to provide to a user. The determining comprises detecting a lowest segment the user is a member of, whereby each segment level corresponds to a level of testing, and selecting a variant from a corresponding set of one or more variants of the lowest sub-segment, a chained variant of a parent segment, or a control value. The modeling manager transmits a response to an experience component that includes the selected variant, and the experience component causes presentation of the experience with the selected variant.

Abstract: In an example embodiment, a user's session sequence data is utilized to provide a universal member representation that achieves one or more of the following goals: 1. Provides a user-level representation that enables the prediction of future actions based on historical interactions within different domains 2. Provides a user representation that allows better clarification of user intent (e.g., network builder, job seeker, profile scraper, etc.) 3. Members with similar/behaviors/intent are easily identified 4. Less sensitivity to activity levels of members.

Abstract: Methods, systems, and computer programs are presented for implementing an artificial-intelligence modeling utility system. One method includes receiving, by a modeling manager, a schema from an experience module that implements features of an online service. The modeling manager manages a plurality of machine-learning (ML) models, provides a user interface (UI) based on the schema for entering experiment parameter values, and configures one or more ML models for the experiment. The experiment is initialized, and during the experiment, the modeling manager receives a request from the experience module for data associated with the experiment and selects one of the configured ML models for providing a response to the request. The response is obtained from the selected ML model based on input provided to the ML model based on the request, and the modeling manager sends the response to the experience. Further, results of the experiment are presented.

Abstract: Embodiments of the disclosed technologies include identifying a user of a network as a possible contributor of digital content to a document that is published via the network; identifying at least two different channels on the network that are each capable of sending, to the user, an invitation for the user to contribute to the document; for each of the at least two different channels, determining respective channel usage data, where the channel usage data includes, for a channel of the at least two different channels, historical data relating to use of the channel by the user to interact with content; for each of the at least two different channels, computing respective channel affinity scores based on the respective channel usage data, where a channel affinity score includes, for a channel of the at least two different channels, an estimate of a likelihood of the user contributing to the document through the channel; based on the respective channel affinity scores, selecting an optimal channel from the at l

Abstract: In an example embodiment, tensor train decompositions are used to create large, personalized layers that are efficient for segment modeling. More particularly, rather than performing learning on an input matrix of training data that contains all segments, and then crossing this matrix with a vector for a particular segment, the matrix is mapped to an N-dimensional tensor, where each of the dimensions corresponds to one of the properties used to compose the segment, which can then be approximated by tensor train decomposition to enable efficient training and scoring.

Abstract: Methods, systems, and computer programs are presented for selecting notifications based on an affinity score between a content generator and a viewer of the content. One method includes capturing interactions of content generators with notifications, received by the content generators, associated with viewer responses to creator-generated content items. The method further includes training a machine-learning model based on the interactions, and detecting a first set of notifications, for a first content generator, associated with interactions of a set of viewers to first-content generator content. The ML model calculates an affinity score between the first content generator and each viewer, and the set of first notifications are ranked based on the affinity scores of the first content generator and the viewer associated with each notification.

Abstract: Technologies for unseen notification handling are described. Embodiments select an initial set of notifications, provide the selected initial set of notifications to a client device, store seen notifications in a first data store, maintain sent but unseen notifications in a second data store that is an in-memory online data store, retrieve a set of the sent but unseen notifications from the second data store, create a list of unseen notifications by combining the retrieved set of sent but unseen notifications with a set of unsent and unseen notifications, generate a set of relevance scores for the list of unseen notifications, create a new version of the list of unseen notifications based on the new set of relevance scores, and provide the new version of the list of the unseen notifications to the client device.

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: Technologies for generating dynamic notification content for notification messages using a machine learned model are provided. The disclosed techniques include identifying an event related to a particular user, where the event has a particular notification type that represents a subject type of the event. Based on the particular notification type of the event, a set of candidate headline and call-to-action combinations corresponding to the particular notification type are identified. Using the machine learned model, scores are calculated for each headline and call-to-action combination in the set of candidate headline and call-to-action combinations. One or more particular headline and call-to-action combinations from the set of candidate headline and call-to-action combinations are selected based upon the scores calculated for each combination of the set of candidate headline and call-to-action combinations.

Abstract: Methods, systems, and computer programs are presented for providing a user experience that facilitates navigation among different topics and articles on a social network. One method includes an operation for identifying a hierarchy of topics, each topic corresponding to a respective subject, where the hierarchy defines relationships between the topics. A first topic page for a first topic is presented in a user interface in the social network. The first topic page includes articles and first options for navigating to topic pages of topics related to the first topic. The method further includes detecting a selection of a first article. In response to detecting the selection, a first article page for the first article is presented in the user interface. The first article page includes details of the first article and second options for navigating to topic pages of topics related to the first article.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Personalized Article Engine that generates respective prediction models for each article in a plurality of candidate articles in a social network system. The Personalized. Article Engine generates a respective article score according to each article's prediction model and at least one feature of a target member account. The Personalized Article Engine generates a plurality of output scores based on combining each respective article score with a corresponding article's global model score. The Personalized Article Engine ranks the output scores to identify a subset of candidate articles relevant to the target member account.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Fast Ranker Engine that identifies global model features present in an article in a social network service. The Fast Ranker Engine assembles respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model. The Fast Ranker Engine generates a transformation feature(s) for a prediction model of the article based on the respective fixed vectors.

Abstract: Techniques for selectively transmitting electronic notifications using machine learning techniques based on entity selection history are provided. In one technique, a candidate notification is identified for a target entity. An entity selection rate of the candidate notification by the target entity is determined. Based on the candidate notification, determining a probability of the target entity visiting a target online system. Based on online history of the target entity, a measure of downstream interaction by the target entity relative to one or more online systems is determined. Based on the entity selection rate, the probability, and the measure of downstream interaction by the target entity, a score for the candidate notification is generated. Based on the score, it is determined whether data about the candidate notification is to be transmitted over a computer network to a computing device of the target entity.

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: Technologies for determining whether to send notification messages, from different sources, to a target user are provided. The disclosed techniques include receiving a first notification event from a first notification service and receiving a second notification event from a second notification service. The first and second notification services are different services. Using a machine-learned model to assign a first score to the first notification event and a second score to the second notification event. Based on the first score, a determination is made to generate a first notification message for the first notification event. The first notification message is then sent to a target user. Based on the second score, a determination is made not to generate a second notification message for the second notification event.

Abstract: Techniques for identifying and delivering notifications of user-generated content to network-limited users are provided. In one technique, for each selected target entity that has a limited network, one or more topics associated with the target entity are identified and the target entity is assigned to one or more entity-topic buckets for the identified topics. For each selected content item, one or more topics associated with the content item are identified and the content item is assigned to one or more content-topic buckets for the identified topics. The entity-topic buckets are matched to the content-topic buckets, resulting in assigning, for each selected target entity, zero or more content items to that target entity. For each target entity that is assigned one or more content items based on the matching, a notification is generated and transmitted over a computer network to a computing device of the target entity.

Abstract: Technologies for determining whether to send notification messages, from different sources, to a target user are provided. The disclosed techniques include receiving a first notification event from a first notification service and receiving a second notification event from a second notification service. The first and second notification services are different services. Using a machine-learned model to assign a first score to the first notification event and a second score to the second notification event. Based on the first score, a determination is made to generate a first notification message for the first notification event. The first notification message is then sent to a target user. Based on the second score, a determination is made not to generate a second notification message for the second notification event.

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: Technologies for generating dynamic notification content for notification messages using a machine learned model are provided. The disclosed techniques include identifying an event related to a particular user, where the event has a particular notification type that represents a subject type of the event. Based on the particular notification type of the event, a set of candidate headline and call-to-action combinations corresponding to the particular notification type are identified. Using the machine learned model, scores are calculated for each headline and call-to-action combination in the set of candidate headline and call-to-action combinations. One or more particular headline and call-to-action combinations from the set of candidate headline and call-to-action combinations are selected based upon the scores calculated for each combination of the set of candidate headline and call-to-action combinations.

Abstract: The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which help determine a user's likely affinity for consuming content (such as an article) in a particular language presented (or to be presented) in a heterogeneous feed of a social network.