Abstract: Methods, systems, and computer programs are presented for predicting a response probability to a sent notification. One method includes an operation for training respective neural networks to obtain a first, second, and third models. The first model generates an embedding based on member information. The second and third model generate parameters for a distribution function. The first model is used to calculate a member embedding when accessing a notification for a member. Further, the method second model calculates a first parameter value, and the third model calculates a second parameter value based on the member embedding. Further, the method determines, a first probability that the member will visit the online service in response to the notification and a second probability that the member will visit without sending the notification. The method further includes determining to send the notification based on the first probability and the second probability.

Abstract: Methods, systems, and computer programs are provided for presenting career information based on career transitions of members. One method comprises generating, using a machine-learning (ML) model, an embedding for a current job position of a member of an online service. The model is obtained by training a ML program with training data for job transitions of members. Further, the method includes generating, by the ML model, embeddings for career transitions, of members of the online service, that occurred within a predetermined time period. For each career transition, a similarity value is calculated between the embedding of the career transition and the embedding for the current job position. Further, the method includes operations for ranking the career transitions based on the similarity values, generating a career insight for the member based on the ranked career transitions, and causing presentation of the career insight on a user interface.

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: This application relates to a vaporization device. The vaporization device includes a housing, a top cap, a heating assembly, and a storage compartment. The housing and the top cap define the storage compartment. The top cap surrounds the heating assembly.

Abstract: This application relates to a vaporization device. The vaporization device includes a housing, a top cap, a heating base, a heating assembly, a first channel, a storage compartment, and a filter screen. The heating base has a first opening. The heating assembly is located between the top cap and the heating base. The housing and the top cap define the first channel and the storage compartment. The first channel is in communication with the outside through the first opening. The filter screen covers the first opening.

Abstract: A method can include determining a first probability that a first member of members of a website will visit the website within a specified time window if the first member is provided an intervention at a specified time, determining a second probability that the first member will visit the website within the specified time window without being provided the intervention, determining a difference between the first and second probability, and in response to determining the difference is greater than a first specified threshold, providing the intervention at the specified time.

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: This application relates to a vaporization device. The vaporization device includes a housing, a top cap, a heating assembly, and a storage compartment. The housing and the top cap define the storage compartment. The top cap surrounds the heating assembly.

Abstract: This application relates to a vaporization device. The vaporization device includes a housing, a top cap, a heating base, a heating assembly, a first channel, a storage compartment, and a filter screen. The heating base has a first opening. The heating assembly is located between the top cap and the heating base. The housing and the top cap define the first channel and the storage compartment. The first channel is in communication with the outside through the first opening. The filter screen covers the first opening.

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 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: Technologies for determining whether to send a notification to an entity is provided. Disclosed techniques include receiving entity features describing attributes related to observed entity sessions. A set of entity-specific session features values may be generated from the received entity features. A session-quality prediction model may be generated using the set of entity-specific session feature values. The session-quality prediction model may determine an expected session score for a new entity session for an entity, where the expected session score describes a level of interaction for the new entity session. A notification may be received for a particular entity. The session-quality prediction model may be used to determine the expected session score for a new entity session for the particular entity. A determination may be made as to whether a notification should be sent to the particular entity based upon the expected session score for the new entity session.

Abstract: Technologies for determining whether to send a notification to an entity is provided. Disclosed techniques include receiving entity features describing attributes related to observed entity sessions. A set of entity-specific session features values may be generated from the received entity features. A session-quality prediction model may be generated using the set of entity-specific session feature values. The session-quality prediction model may determine an expected session score for a new entity session for an entity, where the expected session score describes a level of interaction for the new entity session. A notification may be received for a particular entity. The session-quality prediction model may be used to determine the expected session score for a new entity session for the particular entity. A determination may be made as to whether a notification should be sent to the particular entity based upon the expected session score for the new entity session.

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: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of vectors representing attributes of a set of entities. For each vector in the set of vectors, the system generates a set of signatures from the vector. The system also periodically modifies a technique for generating the set of signatures to update the set of signatures for the vectors. The system then uses the set of signatures to identify a subset of the vectors with a matching signature. Finally, the system outputs the subset of the vectors for use in selecting candidates for matching to an additional entity.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a knowledge graph containing a set of job transitions for members of an online network. Next, the system receives a career path query for a member of the online network. The system then uses member features for the member and parameters of the career path query to identify one or more paths in the knowledge graph that match the career path query.

Abstract: A method can include determining a first probability that a first member of members of a website will visit the website within a specified time window if the first member is provided an intervention at a specified time, determining a second probability that the first member will visit the website within the specified time window without being provided the intervention, determining a difference between the first and second probability, and in response to determining the difference is greater than a first specified threshold, providing the intervention at the specified time.

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.